In the modern world, progress means unemployment. Recent events in the US show that fear of the wreckage of progress is beginning to affect major political movements in the developed world, although it’s unlikely that the new champion of the mythical “white working class” is going to ease the problems they are supposed to be facing. And whatever the particular racial composition of the working classes of the developed world, it is certainly true that they are facing challenges to their economic security, both now and in the future. Furthermore, if we are to move towards a post-scarcity world these challenges are going to be a lot worse. If the developed world makes the right decisions in the next 15 years (I think we can rest assured it won’t) we could see a world of self-driving cars and vat-grown meat, powered by renewable energy from sun, sea and sky that destroys jobs in the fossil fuel sector forever. In some ways we are close to a post-scarcity society – for example, the CSIRO estimates that the Australian coast line holds 8 times the energy required to power all of Australian society – but the changes we make to get there are going to have huge economic and social impact. Beyond the job losses and their cultural impact, what does it mean for Trump’s mythical “white working class” man (it’s always a man), who drives a big pick up truck, works in a coal mine and loves steak, to lose his job in the mines and see his children eating factory-grown meat and driving automated cars?

My own father is a model example of this problem. My father left school at about 15 to start an apprenticheship as a typesetter, and aside from a brief break to work as a hydatids control officer in New Zealand, worked for 40 years as a typesetter until computers destroyed his entire industry in the late 1980s. Finally he was sacked from his job in a small Australian country town, with no severance pay or future, and forced onto unemployment benefits in his early 50s. As a result our house was repossessed, he declared bankruptcy and returned to the UK to live on unemployment benefits, leaving me to fend for myself at the age of 17. This was emblematic of the devastation that computers wrought on this industry in the 1990s, and basically an entire generation of men were driven out of work and replaced by young university graduates with computers. My understanding is that subsequent shake-ups in the industry saw it further consolidated so that the small company my father worked for was probably also extinguished, and replaced with, first, print distribution centres in the big cities, and then print-on-demand services. Now the work of probably 100 typesetters is done by just one person handling print requests from professionals using word software. For my father (and his family) nothing about this story is good, but from an economic and industrial perspective this is exactly what needed to happen, and I benefit from it all the time in the form of cheap printed books and the ease of just emailing a file to Kinko’s and getting it a day later, instead of having to deal with a cranky old bigot like my father whenever I want to print a report. Win! Except for my father and his family …

For my father, thrown onto the dole queue at 50, there was really no solution to this problem. Nobody hires 50 year old men into entry-level positions, and there was no work in his industry anymore, which was in freefall. Sure he could have tried to get work as a taxi driver or some other kind of alternative industry, but these all have barriers to access and they don’t tend to pay entry-level workers the salary they need to support a family and a mortgage. There was no gig economy in the 1990s (nor would a gig economy support the lifestyle needs of a 50 year old man with a family). Like most working class men of his era, he didn’t have the capital to set up his own business, and the only business he could have set up was in any case being systematically destroyed by the computer age. To be clear, my father tried to keep ahead of the game in his field – he wasn’t a slacker, and for example my earliest experience of computers for work was the Mac he brought home in 1988 that didn’t even have a hard drive, on which he was teaching himself to do typesetting tasks (I think he used Adobe products even then!). But staying ahead of the game doesn’t work in an industry slated for destruction, and even in an industry where he might have been able to set up consulting work opportunities the chances of success were limited. Many economists would suggest that this destructive process is liberating, freeing up people like my dad to find new opportunities – to sink or swim in the new economy – but the reality is that when you lose your job with a mortgage and family, in your fifties, in a country town, you don’t swim. You sink. Which is what my dad did, very rapidly.

If we are to move to a post-scarcity society there is going to be a lot more of this, and a lot of it will be more destructive than what I witnessed with my father. The coal death spiral is going to be fast and brutal, and the men who emerge from their last shift in those mines are not going to have alternative work, since they have no education, no skills and no other work. In my father’s case, we lived in a country town that was held up by one industry – the local lead smelter – and that too is now sinking, leaving pretty much everyone else in the town in the same situation as my father. The move to a post-scarcity society has turned that town to a wasteland, and everyone in it is going to have to sink or swim in the new economy.

But should they?

The fundamental problem here is that we are moving towards a society that doesn’t have enough work, in a society that values people only based on their labour. Cast about through the language with which political economics describes what happened to my father and you won’t find a positive term. You’ll hear about men “thrown on the scrapheap”, about “long term welfare dependency” and “cycles of poverty”. You won’t hear men like my dad described as “liberated by technology” or “freed from work”. You won’t hear about how their self-worth was improved by having time to go to flower-arranging classes, and attend to their stamp collecting duties. The only people who are respected for having lots of free time for community work are young people and rich people. Working men are expected to work. But as we move towards a post-scarcity society, what are we to do with all these people we cast into this world of negative phrases and bad stereotypes and empty futures?

In the UK/Australian framework, my father had access to welfare. This meant he lived in a trailer park, earning perhaps 10% of his income as a full-time employee, forced into humiliating rituals of job-seeking and “signing on” to get his meagre payment, even though everyone involved in constructing and managing this system – from Margaret Thatcher down – knew that he would never get another job. Everyone also knew it wasn’t his fault, but you could spend years trawling through the rhetoric of the politicians, the newspaper columnists, and hate radio, and you would never hear talk about people on unemployment because their job was destroyed by a businessman’s strategy – you only hear about dole bludgers, the undeserving poor, people who can’t be bothered to pull themselves up by their bootstraps. Into this world fell my father, proud working man, never to work again, to live on scrapings from the bottom of the government’s deficit-financed barrel.

That isn’t really right, is it?

But we’re going to see a lot more of this, so we need to start thinking about how to handle it. In particular, we need to recognize that as we abolish whole industries with sweeps of policy, we’re going to create more unemployed than we can find jobs for. We need to start talking about these people not as victims of structural readjustment, but as beneficiaries. Instead of bemoaning their fate, we need to welcome it, and treat them accordingly. Instead of telling my father he was thrown on the scrapheap, we should be saying to him, “congratulations! Technology abolished your job! The rest of your life is yours now, thanks for all your effort!” But we can’t do this if we don’t back it up with a proper respect for his material conditions. If we’re going to move to a world of infinite energy supplied by the sun, using solar panels constructed by a machine and monitored by a single guy who manages a solar farm big enough to power a city, we’re going to have to find a better way of dealing with all the coal miners and gas extractors that is better than saying “sorry!” and giving them a meagre welfare payment. So here are two proposals for how to manage the shift to a post-scarcity society, that are based in the reality of where we’re heading, rather than a behavioralist economist’s ideal of a kill-or-be-killed employment market.

  1. Accept the reality of job losses and growing unemployment: Rather than simultaneously treating structural adjustment as a disaster for workers while also demanding they get another job, any job, recognize that people done out of a job by the movement towards a world of no work are the beneficiaries of that move, and the first new citizens of the post-scarcity era. Identify industries that are obviously being destroyed – whether by offshoring, technology, or policy design – and offer specific rescue packages for the workers involved. Not stupid retraining packages based on the pretense that a 50 year old guy kicked out of the only industry he ever knew can ever work again, but real maintenance packages. Say to these men and women, “thanks for your years of work. Progress means your industry is gone, but we appreciate your efforts, and we understand this is a big change, so we’re going to support you.” Provide protection for their homes and incomes, and offer them the chance to retire early with dignity. Don’t insult them by treating them as if they were a 20-something dole-bludging surfer taking 6 months off the labour force to find the waves – offer them a real readjustment package that says “thanks, we appreciate your work, and we don’t need it any more, here’s your reward for a job well done.” Begin to build a class of post-scarcity citizens, not a class of post-adjustment wash outs.
  2. Consider education as a job, not preparation for a job: My father left school at 15 to pursue a career in an industry that was destroyed around him in a few years when he was in his 50s. But a 9 year education is not enough to get by in a modern society – this is a sacrifice he made in his youth to support an economy that changed around him. After his industry failed he spent the rest of his working-age years languishing, with nothing much to do, viewing the world through the lens of a working man with very little education. In the modern world we need as many people as possible to have the best possible education, so why not send him back to school? The government could have said “Thanks for your efforts, we realize that you left school at 15 to help society grow, and now we don’t need your work anymore and we don’t think that’s a fair exchange. Why don’t you go back to school and make up all those years you lost? And if you finish school and you’ve got the thirst for it, we’ll support you through university as well.” Of course, in many developed countries there is no actual barrier to a 50-something dude going back to lower high school – but we know they won’t do that without support, because it just doesn’t work that way. So support them, and make sure that their 40 years of contribution to society doesn’t hold them back from enjoying the same education as even the lowest surfie stoner in the modern world. And if this means that my father spends the last 15 years of his working life going all the way from lower high school to a PhD, then retires and never does anything with it, so what? Our society can afford it.

This is the reality of the modern world. We can afford so much more than we give out. The wealth my father’s efforts generated over his career would have been way more than sufficient for him to be retired 15 years early, the mortgage on his house supported by the government, and an education thrown in for free. He worked hard for some of the biggest publishing companies in the UK and Australia, massive profit makers whose role in the economy was significant. They no doubt paid (or should have paid) more than sufficient taxes to reimburse him for his labour once they no longer needed him. And if we are going to move to a world where most jobs are no longer necessary due to science, automation, or the need to abolish certain industries, we need to recognize that people like my father will be the first denizens of the brave new world we’re creating. We need to reward them, not punish them, for their service. Furthermore, we need to consider the possibility that even with the best, most perfect industry policies in the world, we will only create 1 job for every 2 we destroy – in which case we are going to be permanently increasing the size of the non-working population. So we need to start thinking about maintaining them, not as a burden on the rest of society, not as people who just won’t get a job, but as the forerunners of a society without work.

We are heading towards a society without work. The first people to experience that society are the long-term unemployed and the unemployed older workforce. If we don’t find a way to treat them as full citizens, and to ensure they can engage in society as full citizens – with accompanying salaries and bonuses – we need to realize that sometime in the future we are going to be living in a society with a very small number of wealthy workers and a very large number of poor unemployable people. Such a society is not sustainable, and in some ways, if the rhetoric about his voters is true, Trump is a sign of what will happen to us if we don’t deal with this issue.

Technology is intended to liberate us from labour. We call them labour-saving devices for a reason. But ultimately we need to recognize that once you have liberated a certain number of people from labour, you have created a new, non-working society, and you need to find a way to manage it. We want a post-scarcity society, not a post-happiness society. So let’s start thinking about ways to reward people for a lifetime of labour, rather than punishing them for picking the wrong industry 40 years ago.

 

Hot on the heels of a (probably wrong) paper on ivory poaching that I criticized a few days ago, Vox reports on a paper that claims schools that give away condoms have higher teen pregnancy rates. Ooh look, a counter-intuitive finding! Economists love that stuff, right? This is a bit unfortunate for Vox since the same author has multiple articles from 2014 about rapidly falling birth rates that are easily explained by the fact that teenagers are really good at using contraceptives. So which Vox is correct, 2014 Teens-are-pregnancy-bulletproof Vox that cites national pregnancy and abortion stats, or 2016 give-em-condoms-and-they-breed-like-rabbits Vox that relies on a non-peer-reviewed article by economists at NBER? Let’s investigate this new paper …

The paper can be obtained here. Basically the authors have found data on school districts that did or didn’t introduce free condom programs between 1989 and 1993, and linked this with county-level information on teen birth rates over the same period. They then used a regression model to identify whether counties with a school district that introduced condom programs had different teen pregnancy outcomes to those that didn’t. They used secondary data, and obtained the data on condom distribution programs from other journal articles, but because population information is not available for school districts they used some workarounds to make the condom program data work with the county population data. They modeled everything using ordinary least squares (OLS) regression. The major problems with this article are:

  • They modeled the log of the birth rate using OLS rather than directly modeling the birth rate using Poisson regression
  • Their tests based on ratios of teen to adult births obscures trends
  • They didn’t use a difference in difference model

I’m going to go through these three problems of the model, and explain why I think it doesn’t present the evidence they claim. But first I want to just make a few points about some frustrating weaknesses in this article that make me think these NBER articles really need to be peer-reviewed before they’re published.

A few petty complaints about this article

My first complaint is that the authors refer to “fighting AIDS” and “AIDS/HIV”. This indicates a general lack of familiarity with the topic: in HIV research we always refer to the general epidemic as the HIV/AIDS epidemic (so we “fight HIV/AIDS”) and we only refer to AIDS specifically when we are referring to that specific stage of progression of the disease. This isn’t just idle political correctness: patterns of HIV and AIDS differ widely depending on the quality of notification and the use of treatment (which delays progress to AIDS), and you can’t talk about AIDS by itself because the relationship of AIDS and HIV prevalence depends highly on the nature of the health system in which the disease occurs. The way the authors describe the HIV epidemic and reponses to it suggests a lack of familiarity with the literature on HIV/AIDS.

This sloppiness continues in their description of the statistical methods. They introduce their model as follows:

Condom model

But on page 10 they say that the thetas represent “county and year dummies” and that the Tc represents “county-specific trends”. These are not dummies. A “dummy” is a variable, not a parameter, and “dummies” for these effects should be represented by an X multiplied by a theta. In fact the theta and Tc are parameters, and in any kind of rational description of a statistical model this model is written wrong. It should be written with something like ThetacXc where Xc is the dummy[1].

This kind of sloppiness really offends me about the way economists describe their models. This is a simple OLS regression of the relationship between the log of birth rate and some covariates. In epidemiology we wouldn’t even write the equation, we would just list the covariates on the right hand side. If anyone cares about the equation, it’s always the same and it’s in any first year textbook. You don’t make yourself look smart by writing out a first year sociology equation and then getting it wrong. Just say what you did!

So, with that bit of venting out of the way, let’s move on to the real problems with the article.

Another model without Poisson regression

The absolute gold standard correct method for modeling birth rates is a Poisson regression. In this type of equation we model counts of births directly, and incorporate the population as an offset. This is a special case of a generalized linear model, and it has a special property that OLS regression does not have: the variance of the response is directly related to the magnitude of the response. This is important because it means that the uncertainty associated with counties with small numbers of births is not affected by the counties with large numbers of births – this doesn’t happen with OLS regression. Another important aspect of Poisson regression is that it allows us to incorporate data points with zero births – zero rates are possible.

In contrast the authors chose to use an OLS regression of the log of the birth rate. This means that there is a single common variance across all the observations, regardless of their actual number of births, which is inconsistent with the behavior of actual events. It also means that any counties with zero births are dropped from the model, since they have no log value. It also means that there is a direct linear relationship between the covariates on the right hand side of the model and the outcome, whereas in the Poisson regression model this relationship is logarithmic. That’s very important for modulating the magnitude of effects.

The model is, in fact, completely inappropriate to the problem. It will give the wrong results wherever there are rare events, like teenage births, or wherever there are big differences in scale in the data – like, say, between US counties.

Obscuring trends with a strange transformation

I mentioned above that the article also uses the ratio of teen to adult births (in age groups 20-24) to explore the effect of condom use. Figure 1 shows the chart they used to depict this.

Figure 1: The weird condom diagram

Figure 1: The weird condom diagram

 

Note that the time axis is in years before and after implementation of the program. This is a highly deceptive figure, because the schools introduced condom programs over 4 years, from 1989 to 1993. This means that year 0 for one school district is 1989, while for another it is 1992. If teen births are increasing over this period, or adult births are decreasing, then the numbers at year 0 will be rates from four different years merged together. This figure is the mean, so it means that four years’ worth of data are being averaged in a graph that only covers ten years’ worth of data. That step at year 0 should actually occur across four different points in time, within a specific time trend of its own, and can’t be simplified into this one diagram.

Note that the authors only show this chart for the schools that introduced a condom program. Why not put a similar line, perhaps in a different color, for school districts that didn’t? I suspect this is because the graph would contradict the findings of the model – because either the graph is misrepresentative of the true data, or the model is wrong, or both.

This graph also makes clear another problem with this research: the authors obviously don’t know how to handle the natural experiment they’re conducting, since they don’t know how to represent the diverse start points of the intervention, or the control group.

Lack of a difference in difference model

The authors include a term for the effect of introducing condom distribution programs, but they don’t investigate whether there was a common effect across condom distribution and non-condom distribution regions. It’s entirely possible that school districts without condom distribution programs also saw an increase in teen pregnancies (1989 is when MTV came out, after all, and all America went sex crazy. It’s also the year of Like a Prayer, and Prince’s song Cream was introduced in 1991. Big things were happening in teen sexuality in this period, and it’s possible these big things were way bigger than the effect of government programs.

Statistics is equal to any challenge, though[2]. We have a statistical technique for handling the effect of Miss Calendar grooving on a wire fence. A difference-in-difference model would enable us to identify whether there was a common effect during the intervention period, and the additional effect of condom promotion programs during this period. Difference-in-difference models are trivial to fit and interpret, although they involve an interaction term that is annoying for beginners, and they make a huge difference to the interpretation of policy interventions – usually in the direction of deciding the intervention made no difference. Unfortunately the authors didn’t do this, so we see that there was a step change in the intervention group, but we don’t see if there might have been a similar step change in the control group. This effect is exacerbated by having county-specific time trends, since it better enables the model to adapt to the step in the control group through adaptively changing these county-specific trends. This means we don’t know from the model if the effect in the intervention group was really confined to the intervention group, and how big it really was.

The correct model

The correct model for this problem is a Poisson regression modeling teen births directly with population as an offset, to properly capture the way rates change. It would be a difference-in-difference model that enables the effect of the condom programs to be extracted from any general upward or downward steps happening at that time. In this model, figure 1 would be replaced by a spaghetti plot of all the counties, or mean curves for intervention and control not rescaled to ensure that the intervention happens at year 0 for all intervention counties, which is misleading. Without doing this, we simply have no evidence that the condom distribution programs did what the authors claimed. The ideal model would also have a further term identifying whether a condom program did or didn’t include counselling, to ensure that the authors have evidence for their claim that the programs with counselling worked better than those without.

I’m partial to the view expressed that counselling is necessary to make condom programs work, but Vox themselves have presented conflicting evidence that teenagers are perfectly capable of using condoms. Given this, explicitly investigating this would have provided useful policy insights. Instead the authors have piled speculation on top of a weak and poorly-designed statistical model. The result is a controversial finding that they support only through very poor statistical modeling.

The correct model wouldn’t have been hard to implement – it’s a standard part of R, Stata, SPSS and SAS, so it’s unlikely the authors couldn’t have done it. It seems to me that this poor model (and the previous one) are indicative of a poor level of statistics and research design teaching in economics, and a lack of respect for the full diversity of statistical models available to the modern researcher. Indeed, I have a Stata textbook on econometrics that is entirely OLS regression – it doesn’t mention generalized linear models, even though these are a strong point of Stata. I think this indicates a fundamental weakness in economics and econometrics, and leads me to this simple bit of advice about models of health and social behavior prepared by economists: they’re probably wrong, and you shouldn’t trust them.

I hope I’m wrong, and Vox don’t keep vexing me with “explainers” about research that is clearly wrong. I don’t hold out much hope …


fn1: for those digging this far, or who often stumble across this horrible term in papers they read, a “dummy” is just a variable that is either 0 or 1, where 1 corresponds to the event of interest and 0 to not the event of interest. In epidemiology we would just say “we included sex in the model”. In economics they say “we included a dummy for sex.” This is just unnecessary jargon.

fn2: Except the challenge to be fun.

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Today the Guardian reported on a new study that claims a large sale of legal ivory in 2008 actually led to an increase in illegal elephant poaching. Basically in 2008 China and Japan were allowed to pay for a large stockpile of legally-obtained ivory, in the hopes that this would crash the market and drive ivory traders out of business. Instead, the study claims, the sale led to a big increase in poaching – approximately a 66% increase in elephants killed, according to the study. This is interesting because it appears to put a big dent in a common libertarian idea for preserving endangered species – that allowing a regulated trade in them would lead to their preservation. It is also one of those cute findings that puts a hole in the standard just-so story of “Economics 101” that everything is driven by supply and demand. We all know that in reality there are many factors which moderate the effect of supply and demand on crucial markets, and on the surface this study appears to suggest a quite contradictory supply and demand relationship in illegal poaching markets, in which increasing supply boosts poaching. But is it true?

The Guardian report links to the original study, which is held at the National Bureau of Economic Research behind a paywall, but which I managed to get a copy of through my work. I thought I would check the statistical methods and see if the study really did support this conclusion. My judgment is that this study is quite poor, and that the data doesn’t support that conclusion at all, due primarily to three causes:

  • A poor choice of measure for illegal poaching that doesn’t clearly measure illegal poaching
  • The wrong choice of statistical method to analyze this measure
  • The wrong experimental design

I will go through each of these reasons in turn. Where equations are needed, I have used screenshots from the original paper because I’m terrible at writing equations in html. Let’s get started.

The PIKE is a terrible measure of illegal poaching

The study is based around analysis of a data set of “legal” and “illegal” carcasses observed at search sites in 40 countries. Basically a “legal” carcass is an elephant that died on its own, while an illegal one is one that was shot and looted. Apparently poachers don’t bother to clean up the corpse, they just cut off the ivory and run, so it’s easy to see when an elephant has been poached. However, because no one knows the full details of elephant populations, the authors study an outcome variable called the PIKE, which is defined as the ratio of illegal carcasses to total carcasses. In their words (screenshot):

PIKE equation

They say that this enables them to remove the unknown population from the outcome by “normalizing” it out in top and bottom of the ratio. They justify this with a little proof that I am not convinced by, since the proof assumes that probability of discovering carcasses is independent of the number of carcasses, and that legal mortality and illegal mortality are not related in any way. But even if it factors out population, this PIKE measure doesn’t tell you anything about illegal poaching. Consider the following hypothetical scenario, for example:

Imagine a population of elephants in which all the older elephants have been killed by poachers, so only the pre-adult elephants remain. Every time an elephant becomes mature enough to have decent tusks a poacher kills it and the corpse is found. Further, suppose that the population is not subject to predation or other causes of legal mortality – it is young, and the environment is in good shape so there are large stocks of easier prey animals for lions to target. This population is at high risk of collapse due to adults being killed as they mature; indeed, let’s suppose no babies are born because adults are poached as soon as they reach sexual maturity. Thus every time an elephant is killed, the population drops by one towards its inevitable crash.

In this case, at every time point the PIKE would be 1, because there are no legal carcasses. The PIKE will remain 1 until there are no elephants left to die, at which point it will jump to infinity. It doesn’t tell us anything about the impending population collapse.

Consider now a situation where there are a great many more legal deaths than illegal deaths. Denoting illegal carcasses by y and legal carcasses by x, we have y/(y+x) where y<<x. In this case we can approximate the PIKE by y/x, and if e.g. the number of illegal carcasses suddenly doubles we will see an approximate doubling in the PIKE. But suppose y is approximately the same as x. Then we have that the PIKE is approximately 1/2. Now suppose that the number of illegal carcasses doubles; then the PIKE increases to 2/3, i.e. it nowhere near doubles. If the number of illegal carcasses again doubles, it increases to 4/5. But if all deaths drop to 0 it then increases to infinity … So the magnitude of the increase in PIKE is not a direct reflection of the size of the change in poaching, and in at least one case even the direction is not meaningful. That is not a well-designed measure of poaching. It is also scale free, which in this case is a bad thing because it means we cannot tell whether a value of 1 indicates a single illegal carcass or 10 illegal carcasses. Similarly we don’t know if a value of 1/2 corresponds to 1 or a million illegal carcasses; only that however many there are, they are half of the total.

The authors say that this variable is constrained between 0 and 1, but this is not strictly true; it actually has an additional non-zero probability mass at infinity. This strange distribution of the variable has implications for model choice, which leads us to the second problem with their data.

All the models in this study were poorly chosen

The authors choose to model the PIKE using an ordinary least squares (OLS) model with fixed effects for country and a (separate) fixed effect for each year. An OLS model is only valid if the residuals of the model are normally distributed, which is a very strong assumption to make about a variable that has lots of values of 0 or 1. The authors claim their residuals are normally distributed, but only by pooling them across years – when you look at residuals within individual years you can see that many years have much more normally distributed residuals. They also don’t show us the crucial plot of residuals against predicted values, which is where you get a real idea of whether the residuals are well-behaved.

An additional consequence of using an OLS model is that it is possible to predict values of the PIKE that are unphysical – values bigger than 1 or less than 0 – and indeed the authors report this in 5.6% of their data points. This is indicative of another problem – the PIKE shows a non-linear response to increased illegal kills (see my example from 1/2 to 2/3 to 4/5 above), so that for a fixed number of legal kills each additional illegal kill has a diminishing effect on the value of PIKE, but a linear OLS model assumes that the PIKE changes by a uniform amount across its range. Given that the goal here is to identify increases in the PIKE over time, this runs the risk of the model over- or under-estimating the true effect of the 2008 ivory sale, because it is not properly modeling the response of the PIKE score.

The authors try to test this by fitting a new model that regresses ln(illegal carcasses+1) against a function that includes ln(legal carcasses+1) like so:

PIKE alternative model

This introduces a new set of problems. The “+1” has been added to both variables here because there are many zero-valued observations, and ln(0) doesn’t exist. But if there are lots of zero-valued observations, adding one to them is introducing a big bias – it’s effectively saying there was an illegal carcass where previously there wasn’t one. This distorts low numbers and changes the patterns in the data. The authors claim, furthermore, that “The coefficient on legal carcasses φ will be equal to unity if the ratio of illegal carcasses to legal carcasses is fixed”, but this is both nonsensical and obscures the fact that this model is no longer testing PIKE. It’s nonsensical because that is not how we interpret φ. If φ=1, then we can rewrite their equation (8) so that the left hand side becomes the natural logarithm of (illegal carcasses+1)/(legal carcasses+1). Then we are fitting a linear model of a new variable that is not the PIKE. We are not, however, assuming the ratio of illegal carcasses to legal carcasses is fixed. If φ is not 1, we are modeling the natural logarithm of (illegal carcasses+1)/(legal carcasses+1)^φ. The ratio here is still fixed, but the denominator has been raised to the power φ. What does “fixed” even mean in such a context, and why would we want to model this particular strange construction?

The authors do, finally, propose one sensible model, which is similar to equation (8) (they say) but uses a Poisson distribution for the illegal carcasses, and still fits the same right hand side. This is better but it still distorts the relationship between illegal and legal carcasses by adding a 1 to all the legal (but not the illegal) carcasses. It also doesn’t properly account for elephant populations, which is really what the legal carcasses serve as a proxy for. There is a much better way to use the legal carcass data and this is not it.

Finally there are two other big problems with the model: It uses fixed rather than random effects for country and site, which reduces its power, and also it doesn’t include any covariates. The authors instead chose to model these covariates separately and look for similar spikes in specific possible predictors of ivory usage, such as Chinese affluence. The problem with this is that you might not see a strong spike in any single covariate, but multiple covariates could move together at the same time to cause a jump in poaching. It’s better to include them in the model and report adjusted poaching numbers.

The wrong experimental design

An expert cited in the original article noted this interesting fact:

The Cites spokesman also noted that there had never been a one-off sale of rhino horn: “However, the spike in the number of rhinos poached for horn largely mirrors what has been seen with ivory. The illegal killing of rhino for its horn in South Africa alone increased from 13 in 2007 to close to 1,200 last year.”

This suggests that there has been an upsurge in illegal poaching across Africa that is independent of the ivory sale, and could reflect changing economic conditions in Africa (though it could also reflect different markets for ivory and rhino horn). It’s possible to test this using a difference-in-difference approach, in which rhino poaching data is also modeled, but is treated as not having been exposed to an intervention. The correct model specification then enables the analyst to use the rhino data to estimate a general cross-species increase in poaching; the elephant data identifies an additional, elephant-specific increase that could be said to be due to the ivory sale. The authors chose not to do this, which means that they haven’t rigorously ruled out a common change in poaching practice across Africa. If the CITES spokesman’s point is correct, then I think it likely that we would conclude the opposite to what this study found: that compared to rhinos, elephant poaching did not increase nearly as much, and in fact the ivory sale protected them from the kind of increased poaching observed with rhinos.

Indeed, it’s possible that there were poachers flooding into the market at around that time for other reasons (probably connected to development and increasing demand in Asia), but after the ivory sale most of them switched to killing rhinos. That would suggest the sale was successful, provided you aren’t judging that success from the standpoint of a rhino.

A better model: Bayesian population estimation followed by Poisson regression

It’s possible to build a better model using this data, by putting the legal carcass data to proper use and then using a correctly-specified Poisson regression model on the illegal carcass data. To see how different the results might then look, consider Figure 1, taken from the Appendix of the paper, which shows the actual numbers of illegal carcasses in each year.

Figure 1

Figure 1: Distribution of illegal elephant kills, 2002 – 2013 (year is above its corresponding histogram)

Does it look to you like the number of elephants killed has increased? It certainly doesn’t to me. Note that between 20 and 50% of observed data are 0 kills in all years except 2002 (which the authors say was the start year of the data, and exclude from their analysis). Can you strongly conclude any change from these figures? I haven’t shown the legal kill data but it is broadly similar in scale. Certainly, if there is any upward step in illegal kills in 2008, it could potentially be explained simply by changes in populations of elephants – if even a small change in elephant density leads to an extra 1 or 2 extra kills per site per year, it would lead to distributions like those in Figure 1. To me it seems likely that the single biggest determinant of elephant kills will be the number of elephants and the number of poachers. If we assume the number of poachers (or the pace of their activity) changed after 2008, then surely we need to consider what happened to the population of elephants overall in 2008. If it declined, then poachers might catch the same number as 2007; if it increased, they would catch more.

The best way to analyze this data is to directly adjust for the population of elephants. We can use the legal kill data to do this, assuming that it is mostly reflective of elephant population dynamics. It’s not easy, but if from published sources one can obtain some estimate of the mortality rate of wild elephants (or their life expectancy), a Bayesian model could be built to estimate total population of elephants from carcasses. This would give a credible interval for the population that could then be used as what is called an offset in a Poisson regression model that simply modeled counts of illegal kills directly against time. The advantage of this is that it uses all 0 count events, because a Poisson model allows for zeros, but it adjusts for the estimated population. I think the whole thing could be done in a single modeling process, but if not one could obtain first a distribution of the elephant population, then use this to simulate many different possible regression model coefficients for the effect of the ivory sale. In this model, the effect of the ivory sale would simply represent a direct estimate of the relative increase in mortality of elephants due to poaching.

Then, to complete the process, one would add in the rhino data and use a difference-in-difference approach to estimate the additional effect of the ivory sale on elephant mortality compared to rhinos. In this case one would find that the sale was protective for elephants, but potentially catastrophic for rhinos.

Conclusion

Based on looking at this data and my critical review of the model, I cannot conclude that the ivory sale led to an increase in poaching. I think CITES should continue to consider ivory sales as a tool to reduce elephant poaching, though with caution and further ongoing evaluation. In addition, based on the cited unnamed CITES spokesman, evidence from rhino culling at the time suggests the sale may even have been protective of elephants during a period of increased poaching; if so, a further big sale might actually crush the business, although there would be little benefit to this if it simply drove poachers to kill more rhinos.

With regard to the poor model design here, it shows a lot of what I have come to expect from economics research: poor definition of an outcome variable that seems intuitive but is mathematically useless (in health economics, the incremental cost effectiveness ratio shows a similar set of problems); over-reliance on OLS models when they are clearly inappropriate; poor model specification and covariate adjustment; and unwillingness to use Poisson or survival models when they are clearly most suited to the data.

I think there is lots of evidence that legal markets don’t necessary protect animals from over-exploitation (exhibit A, the fishing industry), but it is also obviously possible that economic levers of supply and demand could be used to kill an illegal industry. I suspect that more effective, sustainable solutions to the poaching problem will involve proper enforcement of sales bans in China and Japan, development in the regions where poaching happens, and better monitoring and implementation of anti-poaching measures. If market-crushing strategies like the 2008 ivory sale are going to be deployed, development is needed to offer affected communities an opportunity to move into other industries. But I certainly don’t think on the evidence presented here that such market-crushing strategies would have the exact opposite of the intended effect, and I hope this poor quality, non-peer-reviewed article in the NBER doesn’t discourage CITES from deploying a potentially effective strategy to stop an industry that is destroying a majestic and beautiful wild animal.

Over Christmas large swathes of northern England drowned, washed away in a huge flood caused by storms from the Atlantic. The same storms battered the Irish coast, and are now moving up towards the arctic, where the North Pole is expected to be 1C – 30C above the average for this time of year – on 30th December. Towns in the north that do not normally experience flooding, like York and Leeds, were submerged, and some towns on the west coast experienced their second or third major floods in three years. Insurers estimate the cost of the latest floods at 5 billion pounds, and more are expected tonight and tomorrow.

For many people these floods will bring financial ruin, because many people in the affected areas were no longer able to obtain flood insurance – the area they live in was deemed too high risk by the insurance companies, which stopped covering them after the 2011-12 floods. Those floods are estimated to have cost 3 billion pounds, and since then the government has been investing about half a billion pounds a year in flood mitigation measures that clearly were insufficient to handle the latest storms. This withdrawal of insurance comes despite the fact that the government instituted a 10 pound levy on all insurance plans in the UK to subsidize the continued provision of flood insurance to at-risk areas – even that additional support was insufficient to get the insurers to return to Cumbria, so people in that area have been running their businesses uninsured since the last floods.

Now the Environment Agency are talking about learning to live with floods instead of preventing them, because they think the government just doesn’t have the resources to cope with the weather. The first Labour member has broken ranks and demanded that mitigation and recovery funding be taken from the foreign aid budget, citing – of all countries! – Bangladesh as an example of a place that shouldn’t be receiving aid money when British people are at need. Bangladesh, of course, faces a future of flood adaptation measures that make the UK’s look trivial, and part of the reason it is economically unable to handle that future is past British colonial intransigence. But of course now that the UK begins to face its global warming future, solidarity with poorer nations will be one of the first higher ideals to give way.

It won’t be the last though, because this is what adaptation looks like: increasing amounts of resources being devoted to Canute-like strategies to temporarily shore-up defenses against increasingly vicious and uncontrollable natural phenomena, and the most vulnerable people on the periphery left to drown or burn. These unprecedented rains aren’t some kind of aberration or heavenly wrath with no explanation or pattern – they’re the latest manifestation of global warming, and there is much worse to come in our lifetimes. Some people will say they’re worse because of El Nino, but the same thing happened three years ago, and for six months much of Somerset was underwater before this El Nino started. The future is here now, long before everyone expected, and it’s not pretty. As the weather turns on us, what we have to do just to hold it back is going to get a lot worse, and the numbers of people affected – and their anger at the people who can’t fix it – are going to grow.

This extreme weather and its associated damage is coming at a time when our ecosystem is suffering increasing stress from other human interference – draining the water table for unsustainable farming, overfishing, habitat destruction and invasive species as well as increasing pressure for land and basic resources like water. We see these stresses running up against the influence of climate change all the time now, in debates like those in the UK and the US about how much water to sequester for protecting environmental flows in rivers. This combination of stresses means that we have less room to manoeuvre when it comes to adaptation. Californians, for example, have adapted to the drought by draining groundwater, which takes decades or centuries of quality rainfall to replace; in the UK there is pressure to dredge more rivers, but river systems are vital to the health of ecosystems, and damaging these systems through dredging will place other pressures on the environment. Increasingly, adaptation measures that were taken for granted in the past will come into conflict with other land-use practices or environmental safeguards.

The UK’s problem with flooding is a good example of this. To properly manage flooding in this “new normal” of increased rainfall and intense storms is going to require coordinated action all along river systems, and it will have to include setting aside some farmland to flood when rivers overflow. George Monbiot describes how upstream grouse moors and fallow fields will need to change land-use practices to prevent run-off, and the need to restore the health of rivers, rather than dredge them, in order to ensure major rains can be properly managed. Additionally, where previously winter precipitation would be stored as snow and released slowly in spring meltwater, now it will fall as rain and wash immediately off high lands, requiring changes in winter land-use patterns. This is going to create additional pressure on farmland and require new models of cooperation between urban and rural communities that, frankly, I don’t think are possible in the UK’s class-blighted society.

Adaptation is also going to require economic changes that a lot of mainstream economists aren’t going to be happy with. The flood levy obviously hasn’t worked, and the idea that insurers will continue to be able to operate profitably under current market conditions while also providing a useful social service is beginning to look untenable. They are going to need increasingly aggressive protections as climate change worsens, or the government is going to have to take on a bigger role as an insurer of last resort. Farmers who are forced to set aside land for flood plains are obviously not going to be insurable, and communities that are clearly intended to play a role as upstream sacrifices (as happened in parts of York) can’t be expected to insure themselves. It’s hard to see how these wide scale, often transnational environmental challenges can be effectively responded to by piecemeal responses in local areas or single countries, or by isolated market entities like insurance companies. A bigger cooperative model is going to be needed if we’re to preserve the key components of our environment in the near future.

Adaptation vs. mitigation was a key plank of the denialist platform in the 1990s and 2000s, and continues to be pushed by luke-warmers and delayers such as the Breakthrough Institute. It’s important to remember, though, that adaptation in practice means that some people have to sacrifice their livelihoods and sometimes their lives on the frontline of global warming’s impacts. For governments, adaptation is a question of dollars and shifting resources, but for the people who are forced to wade through water in the front room of their business “adaptation” can mean bankruptcy or financial ruin, displacement or – at best, in this current situation – a completely wretched Christmas. As the paid shills for delay and denial shift from braying “it’s too soon, we don’t know if it’s a real risk” to “it’s too late, all we can do is adapt,” we should remember what happened this Christmas in the UK (and also the US mid-west, and the Australian surf coast). Adaptation means some people losing their homes and livelihoods, it means towns flooded or (as happened in Japan earlier this year) entirely washed away. It also means increasing pressure on the environment and ecosystem services we all depend on, and on infrastructure like the collapsed bridge in Tadcaster or the overflowing US sewage works – infrastructure that we have taken for granted in some cases for hundreds of years. Even if we somehow conclude that adaptation is still cheaper than mitigation, we should stop and ask ourselves: is it worth the savings?

Let’s hope 2016 brings a renewed commitment to fix this growing and increasingly dangerous problem, before climate changes washes, burns and blows away all of industrial civilization.

You entrusted your money to people who eat smoked guillemot?

You entrusted your money to people who eat smoked guillemot?

I was in the UK in 2008 and 2009 when the Icesave banking disaster happened, and the UK government rushed to use anti-terrorism legislation to try and protect the money of British investors. There were something like 300,000 “ordinary” British and Dutch investors with money in Icesave accounts, and when the disaster happened all but the first 20,000 pounds or so were not protected by deposit insurance, so the UK government acted to try and protect the full deposits of the savers. I remember this clearly [although, probably not details of dates and money amounts] because one of my colleagues at the time had 120,000 pounds parked in such an account, the proceeds of selling her house, and was looking forward to using the money – inflated by the high interest – to buy her next one, and she was understandably distraught when she woke up to discover it had vanished into volcanic smoke.

I also remember at the time that there was a lot of anger in the British public, not only at Iceland, but also at the British government for guaranteeing the deposits of people who were basically risking their money to get a higher rate of return. I often heard the refrain “they knew the risks” and many people pointed out at the time that higher interest rates usually correspond with higher risk, and these people could have had their money protected if they had taken more reasonable risks in a UK bank. This rhetoric probably wasn’t based entirely in fact, since British deposits weren’t fully guaranteed, and the UK government had to rush to assure large deposits in Northern Rock after it failed[1], but the general rhetorical principle was correct, British banks were safer than Iceland banks and had a correspondingly lower rate of return. The question was asked: should we bail out people who knew the risks they were taking? (Incidentally, I didn’t actually know at that time that a slightly higher rate of interest in a country that I assumed had good banking laws was a sign of higher risk; as a result of the rhetoric of that period I reassessed my involvement in an ING online account that is now defunct).

I can’t easily find articles online from the time that say these things, but I don’t think my memory is wrong. This comment by an academic from McGill University (Canada) makes the point that investors should wear the risk; this blog roundup suggests that many economists thought it was right for Iceland to refuse to protect investors, and indeed Christine Lagarde of the IMF thought Iceland took the right approach. I can’t find any articles directly demanding that deposit holders should carry their risk, but I do remember it being a commonly-stated view at the time, and the view that Iceland did the “right thing” by telling investors to take a haircut is well-accepted, I think, as is the view that it has recovered better than those economies that did not. A subsidiary view, that deposit insurance creates moral hazard, is widely broadcast I think and is consistent with the idea that if you want to get a high rate of return on your deposit you need to be willing to accept the risk that you will lose it, pour encourage les autres. So I don’t think I’m wrong about this perspective and how it was broadcast at the time even if I can’t find written evidence.

The idea that “investors” should wear the risk they take when chasing big profits seems completely reasonable, until one remembers that in this case the investors (and ultimately the creditors) for Icesave included depositors, that is ordinary people who put money in a high-risk/high-return account hoping for a short term gain. It seemed at the time that a lot of people were comfortable with the idea that creditors should just put up with their haircut, and depositors “knew the risks.”

So it’s interesting to compare this rhetoric with the rhetoric surrounding Greece’s recent troubles. Much of the rhetoric about Greece focuses on its profligacy, the easy-spending nature of the Greeks, their corruption, their crazy ideas that they could just keep taking on more debt and spending it however they want. You don’t see much rhetoric (or at least, I haven’t) questioning why people were willing to lend them all this money, and why their creditors are now so heavily exposed. Remember that for every debtor there is a creditor, and the creditor wouldn’t be lending the money if they didn’t want to, i.e. if they weren’t benefiting from it. When Icesave collapsed the greedy motives of the creditors (and, implicitly or explicitly, the depositors who make up a certain proportion of those creditors) was front and centre in the debate, but it’s strangely absent from the Greek debate. We know that in the early stages of its crisis Greece had to take on a lot of public debt to bail out banks that were in trouble; at the time of writing it appears that private debt constitutes about 60bn euros of Greece’s total, which would have been about 30% of the total debt before the collapse. Why were these people lending money to a country that was cooking its books, had apparently obviously unsustainable pension and welfare systems, and an entire population that we are now told were slurping up ouzo down by the beach rather than working 12 hour days like Germans? These creditors didn’t have to lend this money, they could have bought German bonds or Iranian nuclear futures or something more solid and reliable. They loaned money to Greece because up until the crisis Greece’s economy was growing faster than anywhere else in Europe, everyone wanted a slice of that golden Greek sunshine, and basically they thought they could make their motza[2] and get out before the whole shebang went tits-up. i.e., they were greedy. Yet nowhere do we hear tell of their greediness – even though at the same time as their golden goose was turning barren, Icesave depositors were copping flak in the press and the public for being greedy and reckless.

Why is that?

We also shouldn’t stop with these faceless private lenders, who are no doubt lounging around in a gold-plated yacht off some private Greek Island, fluffy white cat firmly en-lapped. We can also wonder why none of this rhetoric of recklessness extends to the dour and responsible Germans. Germany has 60bn Euros sunk in the Greek project, and it is earning a healthy rate of interest. Germany, the country that has never paid its debts, the ultimate trust fund kid, is now strangely insistent on Greece paying its debts, and no one anywhere is questioning why Germany is so exposed to the economy of a country it has deplored as reckless, irresponsible, intransigent and wayward (indeed, worse than Iran if we are to judge by their negotiating results). A handful of eurozone countries have something north of 200 bn Euros sunk into the Greek project, and we now know that they are making a lot of money from this little act of charity: the Guardian’s live blog today tells us that David Cameron is contemplating demanding some of the 1.9bn Euros in profit that the ECB has made from its loans to Greece (though it doesn’t tell us over what period that profit was made). How come this fact – that the eurozone lenders are making fat scads of cash – is not being broadcast widely, as the Icesave depositors’ greedy winnings were being broadcast in 2008? Instead of this morality play, we are constantly reminded that the German taxpayer doesn’t want to have to cough up his or her hard-earned dollars to cover Greek mistakes. Yet right now the German taxpayer is making money from this debacle, so shouldn’t we be instead asking why the German taxpayer tolerates his or her government sinking 60bn Euros into a high-risk, high short-term profit venture in junk bonds? Germany is a responsible country, we’re told, whose taxpayers don’t take risks – at the same time as the media carefully avoids reporting on the big money Germany stands to make if Greece doesn’t default.

The situations aren’t exactly the same of course, and people could argue that the eurozone nations didn’t have a choice – they aren’t loaning this money because they want to, the poor darlings, they’re doing it to save Greece and the euro project. But they did have choices, many choices: they could have told those (primarily French and German) banks to fail, as Iceland did, back at the beginning of the crisis; they could have rushed through some changes to the welfare transfers in the EU to ensure that Greece received direct payments rather than loans[3]; they could have printed money and handed it to the banks, as the UK and US did; they could have raised debt in their own countries, which are much less financially at risk, and provided it as a grant or something; they could have told Greece to find the money on private money markets. But they didn’t, they chose to lend money to Greece on terms that just happen to deliver them large profits – profits that are likely larger than they could have got from e.g. buying each others’ government bonds, or investing in the kind of low-return portfolios that would be politically acceptable to their electorates. And it just so happens that, since they control the mechanism by which Greece generates the repayments of those debts, they are able to turn the screws to ensure the money keeps coming – unlike those investors in Icelandic banks, who have no direct means of control over Icelandic politics and economy (and anyone from Britain who is old enough to know about the Cod Wars should surely know how hard it is to control Iceland!)

And all while this was going on, we were being told about how irresponsible ordinary depositors were to put their money in a bank that had a high interest rate. It’s almost as if the morality underlying the rhetoric depends entirely on the people who took the risks …

Fn1: Northern Rock was then run by famous climate change denialist Matt Ridley, which one should always remember when one is considering how far our modern banks have sunk, and how much one should trust the risk assessment abilities of climate change denialists.

Fn2: This is a Greek word, trust me, I’m Australian so I know Greek slang

Fn3: Something you might argue is hard to do, but it appears that today the leaders of the ESMF have been able to magic up 20 billion euros from the Common Agricultural Policy, in order to find a way to provide rapid finance without leaning on the ECB[4]

Fn4: Which makes one wonder, doesn’t it? Have these people been listening to the Greek government when it tells them how fucked it is? Had they not noticed? They just spent two days arguing with a Greek dude about whether to give him any money, and after they agree they find they don’t have any mechanism to provide the money, and he needs it now and he’s been telling them that for weeks! Perhaps instead of spending that two days arguing, they could have spent it more productively looking for their wallet.

In recent days there has been a tiny bit of discussion on this blog about whether a group of 9 unelected philosopher-kings should be able to decide social issues for 330 million people, so it seems appropriate that I turn my attention briefly to the chaos rolling over Europe and the threat of a Greek exit from the EU. From the outside looking in it seems like the three main powers involved in this shit-show (the European Central Bank, IMF and European Commission) have refused to give any serious ground on their demands, even though these demands are obviously not going to help Greece out of its crisis, and have instead decided to essentially dictate to Greece the terms of its fiscal, labour, welfare and banking policies. Given that they are well aware of how much their austerity policies have failed, and know full well that Syriza was voted in on the promise of no more austerity, it’s just ridiculous bloody-mindedness that drives them to force their ultimatum on Greece. The ECB even appears to have withdrawn its standard emergency credit line for banks experiencing instability, without any justification. They’ve basically made clear to Greece that they won’t accept any political options except those that suit their ideology. This is not how politics works, and it’s no surprise that under this pressure Syriza have decided to tell the troika to jump. Paul Krugman (who for some reason I never normally read) has a particularly deft explanation of this referendum decision:

until now Syriza has been in an awkward place politically, with voters both furious at ever-greater demands for austerity and unwilling to leave the euro. It has always been hard to see how these desires could be reconciled; it’s even harder now. The referendum will, in effect, ask voters to choose their priority, and give Tsipras a mandate to do what he must if the troika pushes it all the way

This is how politics should work, and giving Greece a week of grace to sort this out and set a clear future path would be a good way to indicate respect for its political autonomy. This is also the reason that David Cameron’s promise of an in-out referendum, though insane for Britain, is politically the right thing to do. Tsipras has taken the chance to make sure that his country’s decision is politically validated, and that he can make his final decision about the euro from a position of democratic legitimacy; the leaders of the EU’s main powers are flabbergasted by this, and the troika are confused. It appears that they don’t understand where their authority ends and the democratic demands of the people of Europe begins, and it looks as if a lot of Greek people are going to have to go through a fair amount of pain in order to teach them. This is disappointing, given the states involved are apparently all democratic, and it gives the lie to what I think is increasingly shaping up as the central fiction of the European project: that it can stop another war in Europe.

The EU is a fairweather friend

This isn’t the first piece of brinksmanship that has been deployed by an EU member in recent time. A few weeks ago Italy’s prime minister, Matteo Renzi, threatened to issue Schengen visas to refugees coming from Africa and send them on to other parts of Europe, after it was revealed that not only were other countries doing nothing to help, but German, French and Swiss authorities were turning migrants back at their borders, forcing Italy to manage both the rescue and the housing and welfare of tens of thousands of migrants – even though most of those migrants are hoping to move north to other parts of Europe. Basically Italy had to shoulder this whole burden because the rest of Europe has shown itself unwilling to help its members when they face serious problems. The same could also be said for the UK’s welfare and work problems: it is obvious that the UK is a preferred destination for migrant labour in Europe, because everyone in Europe learns English and the pound is so strong, but the EU has absolutely refused to bend the rules for the UK on welfare and migration issues.

You may not agree with the specific governments on any of these issues (I don’t agree with the UK, for example) but I should hope it’s obvious what the problem here is: the EU member states are fairweather friends. They can carefully hammer out a compromise agreement on a shared issue like the free movement of labour or the role of the ECB that will enable them to handle the normal, stable times, but they are completely unwilling to compromise their own interests for the greater good when extraordinary circumstances roll around. The free movement of labour is fine but sharing the resettlement of refugees is impossible, and will be left for the country that happens to be unlucky enough to get them first; shared work and welfare goals are fine but they absolutely won’t consider an exception for a country that is bearing an unusual proportion of the effects of those rules; stability targets are fine but no one is willing to risk either their ideological purity or their own taxpayers (Germany’s constant petty battle cry) when a shared financial crisis hits one of their weakest members unusually hard. Basically, the countries of Europe are behaving like fairweather friends who pat you on the back and congratulate you when you have a success, and are happy to split the bill at your Friday pizza-and-beer nights, but would rather you didn’t come if you’ve fallen on hard times and might like to skip paying for the odd Friday night. They’re happy to talk about helping you move house, or minding your pets while you visit a sick relative, but strangely they’re all busy when the time comes.

This is funny because the regular refrain we hear from the EU’s main sales merchants is that the EU establishes a bulwark against the risk of a future war in Europe. I’m sorry, but if the countries of the EU can’t come up with a mutually acceptable target for distributing 50,000 refugees among a population of 350 million without being threatened with an ultimatum, it’s unlikely that any one of them are going to pause for even the blink of an eye if war is in their interests. Indeed, while the EU rumbles on with its chaotic and obstinate mismanagement of what should have been a complete non-crisis in Greece, certain countries on the eastern edge are entertaining military antics by a non-EU member (the USA) that threatens to involve them in a war so catastrophic that they’ll all be running to Greece. If this is how you construct an “ever closer union of peoples” that will guarantee peace, then peace must be pretty easy to come by.

The reality is that war isn’t going to happen inside Europe because no one wants it, and the major powers are aging so fast that they are no longer able to field a decent war machine. I think this is great, and one of the many untold benefits of rapid aging, but I don’t think it has much at all to do with the European project, which is looking increasingly like a German/French alternative to colonialism, intended to drive down the competitiveness of the European periphery and ensure the centre gets access to reliable markets and a long-term pool of cheap labour. Students of history might suggest that this is exactly the wrong way to go about ensuring a non-chaotic future: the students of Greece are likely to soon provide an object lesson on the topic.

If the EU wants to retain any kind of democratic legitimacy, its member states need to think about how to rein in their executive, and start giving more credence to the (disparate) complaints of countries like Greece and the UK, about precisely how governance should work in such a confederacy. Because right now it’s looking like a couple of people from primarily northern and western powers think that they can dictate political terms to entire nations on the periphery. That’s empire, not union, and I think people are starting to notice …

Addendum: Joseph Stiglitz also seems to think that the EU is behaving poorly, and Krugman has a couple of pieces pointing out that Greece wasn’t as badly off as we are told, and austerity has really done Greece no favours.

In your bases, pWning your Austerity

In your bases, pWning your Austerity

Following up on yesterday’s post about the new Greek finance minister, Yanis Varoufakis, today I investigated his involvement in computer game economics a little more. I found this article by Brad Plumer, written for the Washington Post in 2012, which describes the growing role of economists in computer gaming. Modern online multiplayer computer games are now so complex that they have their own economies, and small decisions by the game company can have major effects on the economies operating in the game and, by extension through the money players invest in some products, on the real world economy. The decisions can be political decisions – such as a decision by Second Life to ban certain kinds of gambling – or they can be god like interventions, such as when the people running Eve Online decide to change the distribution of resources in their galaxy. Some companies have recognized that they need to understand the consequences of their decisions if they want to keep players happy, and the company running Eve Online appear to have led the pack by assembling a whole team of economists.

Into this fray in 2012 stepped the new Greek finance minister, Yanis Varoufakis. Valve, the company now running Steam, wanted to join together a bunch of different games so that players could trade between game worlds. I’m not sure how this works or why one would want to do it but they seemed to think it was a good idea, but in essence what they were trying to do was set up a kind of European Union, in which different games are sovereign in their own resources and political decisions but not in their own currency. So they employed an expert on currency zone economics – Yanis Varoufakis, the new Greek finance minister. According to Varoufakis in the article, his academic colleagues

know I don’t have any actual interest in video games. But I only need to talk to them for a few minutes, and they quickly get excited, asking, ‘Well, what if you tried this . . . ?’

and I can see the appeal of this. You can run experiments, and learn about how decisions will affect the economy, which can provide useful information outside of the computer game world. Varoufakis has apparently used this to show interesting things about General Equilibrium theory, but interestingly the players quickly realize an experiment is being conducted, and game it. What does that tell us about the way ordinary people react to economic policy even when they don’t know it is coming?

Besides being a fascinating field of study in itself, this tells me interesting things about Varoufakis. While some people seem to see him as a threatening radical, and the Guardian‘s initial reaction to his appointment was to publish a whole run of “sky is falling” quotes from German bankers[1], in the world outside of politics it appears he is seen as a serious and intelligent judge of how to manage monetary unions, to the extent that people who depend on getting this right have paid him to help them do so. My guess is that his work on computer game economics will not register at all to Europe’s deep thinkers, or will even constitute a black mark against his name – further evidence he is not a “serious” economist – but it seems to me that he is someone they should listen to, and probably the only finance minister in Europe who might know something about the fundamentals of the EU process. Perhaps all of Europe could benefit from listening to the experience of Greece’s new finance Minister – if they can see past his party and their biases about Greek.

Varoufakis’s ascension to power politics in Europe also puts computer game economics in the spotlight. Maybe it’s time computer game companies started taking the possibility of economic experiments within their worlds seriously, and presenting their virtual worlds to world leaders as an opportunity to study economics in a safe environment. It appears we can learn a lot about the shortcomings of real world theories by testing how they work in worlds in which we can control the fundamentals, right down to the raw materials. There are many questions in economics that could be answered through interventions in these worlds.

My guess is that the European Union will ignore Varoufakis’s expertise, and even if they wanted to computer game companies will have little intellectual impact on the economics world, even though they offer a unique opportunity to test a wide range of economic theories. A shame, for both Europe and the economics profession. Let’s hope the Europeans listen to the economic aspirations of a bunch of dragon slayers and space pirates, and use the lessons learned to fix their most intractable problems!

fn1: They put these on their execrable blog-formatted “live” news section, so I can’t find them or link them now. Why they thought German bankers would be objective commentators on Greek political appointments is beyond me.