An MYEFO mystery: what’s with the resource tax?

It’s the time of the mid-year Economic Fiscal Outlook (MYEFO) and we’re told that we’re about 11 billion deeper in the red this financial year than we thought, with the treasurer blaming the dropping iron price and the reduced wage growth. I have gone over the MYEFO documents (which are an exercise in obfuscation if ever I saw one), found that wage growth and the dropped iron ore price would ‘only’ cost us 2.3 billion each in this financial year (2014-2015), noted that this was far short of the 11 billion headline, and thus went looking for the ‘real story’.

This threw up the mystery of the resource tax. Here is what it says on table 3.2:

Table 3.2: Impact of Senate on the Budget (underlying cash balance)
Estimates Projections
2014‑15 2015‑16 2016‑17 2017‑18 Total
$m $m $m $m $m
Impact of decision taken as part of Senate negotiations(a)
Repeal of the Minerals Resource Rent Tax and related measures -1,684 -2,334 -1,670 -947 -6,634

which seems to means that the repeal of the minerals resource rent tax (and related measures) is costing us around 2 billion per year. Yet, in the ‘Overview Part’, the MYEFO says “The repeal of the Minerals Resource Rent Tax and other related measures will save the budget over $10 billion over the forward estimates and around $50 billion over the next decade.”.

What is going on?

Update (thanks Chris Lloyd): it seems to be a language issue. Part of the story seems to be that the MYEFO is counting the repeal of the mining tax, which was an election promise, as something the Senate inflicted on the budget, so the 2 billion a year is ‘revenue foregone’. So the MYEFO is blaming the Senate for the outcome of an election promise, using an odd formulation to say that the repeal will save us 50 billion when it seems to imply it would cost us 50 billion. Weird.

Remembering Whitlam

Gough Whitlam was the first prime minister I was aware of. Actually, I recalled yesterday that I had seen every Australian Prime Minister since (up until the current one) in the flesh. What other country is that possible?

I saw Whitlam for the first time, in the flesh as it were, when I was 5 year’s old. It is one of my earliest memories. We were at Coogie Beach. I thought we were there for the clown show but, in fact, we were there to hear Whitlam speak. I remember him shouting at the crowd — that is what one of his speeches sounded like. It is clear in my mind today as I thought about it ever since.

A year or so later, he was part of another very early memory. I remember a newspaper on November 12th 1975 with the headline “Dismissed.” I asked my father what that was about and he told me that the PM had been sacked. I asked why and he said that he had lied. My parents, you might guess, were not Whitlam supporters.

Fast forward another 7 years or so later and I watched the wonderful ABC Mini-series, The Dismissal. It was before the Hawke election. There is a moment in many people’s lives when their political leanings are set. I personally think there is a large element of choice to that — especially for people who have not known personal suffering in their childhoods. That series was my moment. It made me left leaning and pro-economy at the same time. The Whitlam government was both (think pipelines not saving rainforests). I softened each of these since then (in fact, on the environment, I flipped from my 14 year old days). But that series was the moment and I was outraged that it had happened. Suffice it to say, I was no fan of Fraser; at least, not until recently when he joined Twitter and became the voice of reason in Australian politics.

Everyone has a politician that is formative to them. For me, Whitlam was that person at a ridiculously young age. He may have been PM for only 4 years of his 96 but what a 4 years it was.

[Update: my parents tell me that they voted for Whitlam on at least three occasions. So I guess I was wrong about my inference there.]

Scottish independence: a good idea or a bad idea?

Today the people residing in Scotland can decide whether they want to see an independent Scotland or to have Scotland remain in the UK. The betting markets concur with the opinion polls and favour the status quo: the markets give roughly 20% chance that the ‘yes’ vote will win and that Scotland will become independent.

The majority of economists talking about the referendum have focused on whether or not the Scots would be financially better off with their own country, debating things like North Sea oil revenues and currency unions. I think that is a distraction: looking at small and large countries in Europe, you would have to say there is no noticeable advantage or disadvantage to being a small country and that the Scots are hence unlikely to be materially affected in the long run by independence.
Independence is more about self-image and identity than it is about money. Even though the push for independence might well come from politicians and bureaucracies that gain prestige and income if they ruled an independent country, the population deciding on the vote will probably vote on emotional grounds, not economic. Young male Scots appear overwhelmingly in favour of independence; females and old people prefer to keep things the way they are. The latter groups are bigger and are expected to sway the day.

Personally, I have two related reasons to oppose the breaking up of larger countries in Europe into smaller ethnically defined states, not just Scotland, but also Catalonia, the Basque region, the Frisian province, Bavaria, and all the other regions of Europe:

  1. These independence movements are ethnic and hence by definition exclusionary. This is a big concern: large nation states have slowly moved away from the story that they exist for people of the ‘right’ bloodlines and with ancestors who lived in the ‘right’ place. The UK, the US, France, Australia, and even Germany and Spain have moved towards an identity based on stories about what it means to be British, American, French, Australian, etc., rather than a ‘blood and earth’ ethnic nation state story. Speaking tongue-in-cheek, the Brits have an upper lip story, the Americans have an exceptionalism story, the French have been convinced they like reading Proust, the new Australians are told in their citizenship exams that they believe in a fair go, etc. These stories contain treasured national stereotypes, complete with imagined histories. The key thing is that are inclusive, ie any newcomer from another place can participate in such stories. The Australian national anthem is a beautiful example of this super-inclusive attitude as it, almost uniquely, mentions neither ethnicity nor religion as a basis for being Australian. The ethnic stories of the independence movements are, in contrast, exclusionary and hence harmful to the self-image of any migrant. It is a move to a past that we have little reason to be proud of, as it marginalises current and future migrants. The story surrounding Scottish independence is thus not that the Scots are people who like to wear kilts and enjoy haggis, but that they make up the people who have suffered 700 years of oppression by the English. What is a recent newcomer from, say, Poland to do with such a self-image but conclude that they do not really belong there?
  2. The mixing of populations inside the UK due to factors like work, marriage, and retirement, now means that large parts of the ‘Scots’ live elsewhere and large parts of the population living in Scotland come from elsewhere. So there are reportedly close to a million Scottish-born people living elsewhere in the UK, and half a million people living in Scotland who were in fact born in England. Becoming independent from those ‘evil English that oppressed us for 700 years’ means marginalising both the 10% of the resident Scottish population actually born in England and putting a traitorous label on the million that decided the supposed oppressors were people you could marry and work with. If we consider the fractional heritage that nearly every UK citizen has, with some ancestors from Scotland and some from elsewhere, nearly every UK citizen will then almost arbitrarily be ‘forced to choose’ whether their fractional Scottishness counts as 1 or as 0. This is a problem: the roughly 5% of my ancestry that is probably Scottish does not want to be alienated from the 45% that comes from other parts of the British Isles!

These two reasons amplify each other: the damage that an ethnic-story based independence movement does gets amplified if the mixing is very large and is somewhat less of a factor when there is very little mixing.

What goes for Scotland goes doubly for many other regions in Europe: for instance, I believe some 40% of the people living in Catalonia are born outside of Catalonia and in other Spanish regions. The population mixing between regions of France and Germany is similarly large. The reality of a joint national economy is that the populations have internally mixed and artificially going ‘back’ to supposedly ethnically pure groups that define themselves in terms of adversity to the others is a regression.

It is of course these mixed populations that provide a counter-weight to any break-away movement, and they provide clear policy prescriptions for those who want to keep their countries intact: mix the population around to emasculate those who want to pull any geographic ethnicity card.

So I will be hoping that the betting markets are right, that mixing populations over the last few decades has done its integrative job, and that the ‘No’ vote wins.

 

How did the Snowden revelations impact behaviour?

This week the Australian government announced what seems to be an extraordinary piece of legislation.

Spies who leak sensitive information will face tough new penalties of up to 10 years’ jail and internet firms could be forced to store customers’ data for up to two years under sweeping national security reforms.

Prompted in part by the leaks from renegade US intelligence contractor Edward Snowden, the Abbott government will on Wednesday introduce legislation clamping down on intelligence officers who leak to journalists, lawyers and other members of the public.

Separately, Attorney-General George Brandis has given his strongest hint yet that the government will move ahead with controversial ”data retention” laws.

This would mean basic records of internet communications such as emails and Skype calls would have to be stored by providers for up to two years to help intelligence and law enforcement agencies carry out investigations and prosecutions.

Indeed, it appears to go further.

Australian journalists could face prosecution and jail for reporting Snowden-style revelations about certain spy operations, in an “outrageous” expansion of the government’s national security powers, leading criminal lawyers have warned.

A bill presented to parliament on Wednesday by the attorney general, George Brandis, would expand the powers of the Australian Security Intelligence Organisation (ASIO), including creation of a new offence punishable by five years in jail for “any person” who disclosed information relating to “special intelligence operations”.

The person would be liable for a 10-year term if the disclosure would “endanger the health or safety of any person or prejudice the effective conduct of a special intelligence operation”.

Suffice it to say this seems to fly way over the bar that we would normally want to protect free speech and freedom of the press; not that these are enshrined in the Australian constitution in the way they are in the US.

To appreciate the impact of policies designed to curtail the dissemination of disclosures, it is useful to actually go to the evidence. A few months ago, Alex Marthews and Catherine Tucker provided that evidence in this paper. Here is the abstract:

This paper uses data from Google Trends on search terms from before and after the surveillance revelations of June 2013 to analyze whether Google users’ search behavior shifted as a result of an exogenous shock in information about how closely their internet searches were being monitored by the U. S. government. We use data from Google Trends on search volume for 282 search terms across eleven different countries. These search terms were independently rated for their degree of privacy-sensitivity along multiple dimensions. Using panel data, our result suggest that cross-nationally, users were less likely to search using search terms that they believed might get them in trouble with the U. S. government. In the U. S., this was the main subset of search terms that were affected. However, internationally there was also a drop in traffic for search terms that were rated as personally sensitive. These results have implications for policy makers in terms of understanding the actual effects on search behavior of disclosures relating to the scale of government surveillance on the Internet and their potential effects on international competitiveness.

What this suggests is that moving to a culture and policy regime where it is widely known that citizens are under surveillance has a multiplier effect on their behaviour — making them fearful of investigated terms that they might guess would be part of that surveillance. If you look on page 33-34 of the paper you might well be surprised at how broad that brush is. While one could read this as supporting bans on such disclosures so that the bad guys keep searching and can be surveilled, it is not at all clear who was changing their behaviour. It may just be people who wanted to monitor their own government and perhaps bring to light governmental bad behaviour.

One thing is more certain, this adjustment is suggesting that moves like this one for the Australian government may be actually upsetting more citizens than they think and that might well add up in terms of votes. I’m sure that will be of interest to politicians who think supporting this legislation is innocuous.

How to lie with statistics: the case of female hurricanes.

I came across an article in PNAS (the Proceedings of the National Academy of Sciences) with the catchy title ‘Female Hurricanes are deadlier than male hurricanes’. It is doing the rounds in the international media, with the explicit conclusion that our society suffers from gender bias because it does not sufficiently urge precautions when a hurricane gets a female name. Intrigued, and skeptic from the outset, I made the effort of looking up the article and take a closer look at the statistical analysis. I can safely say that the editor and the referees were asleep for this one as they let through a real shocker. The gist of the story is that female hurricanes are no deadlier than male ones. Below, I pick the statistics of this paper apart.

The authors support their pretty strong claims mainly on the basis of historical analyses of the death toll of 96 hurricanes in the US since 1950 and partially on the basis of hypotheticals asked of 109 respondents to an online survey. Let’s leave the hypotheticals aside, since the respondents for that one are neither representative nor facing a real situation, and look at the actual evidence on female versus male hurricanes.

One problem is that the hurricanes before 1979 were all given female names as the naming conventions changed after 1978 so that we got alternating names. Since hurricanes have become less deadly as people have become better at surviving them over time, this artificially makes the death toll of the female ones larger than the male ones. In their ‘statistical analyses’ the authors do not, however, control adequately for this, except in end-notes where they reveal most of their results become insignificant when they split the sample in a before and after period. For the combined data though, the raw correlation between the masculinity in the names and the death toll is of the same order as the raw correlation between the number of years ago that the hurricane was (ie, 0.1). Hence the effects of gender and years are indeed likely to come from the same underlying improvement in safety over time.

Using the data of the authors, I calculate that the average hurricane before 1979 killed 27 people, whilst the average one after 1978 killed 16, with the female ones killing 17 per hurricane and the male ones killing 15.3 ones per hurricane, a very small and completely insignificant difference. In fact, if I count ‘Frances’ as a male hurricane instead of a female one, because its ‘masculinity index’ is smack in the middle between male and female, then male and female hurricanes after 1978 are exactly equally deadly with an average death toll of 16.

It gets worse. Even without taking account of the fact that the male hurricanes are new ones, the authors do not in fact find an unequivocal effect at all. They run 2 different specifications that allow for the naming of the hurricanes and in neither do they actually find an effect unequivocally in the ‘right direction’ (their Table $3).

In their first, simple specification, the authors allow for effects of the severity of a hurricane in the form of the minimum air pressure (the lower, the more severe the hurricane) and the economic damage (the higher, the more severe the hurricane). Conditional on those two, they find an insignificant effect of the naming of the hurricanes!

Undeterred and seemingly hell-bent to get a strong result, the authors then add two interaction terms between the masculinity of the name of the hurricane and both the economic damage and the air pressure. The interaction term with the economic damage goes the way the authors want, ie hurricanes with both more economic damage and more feminine names have higher death tolls than hurricanes with less damage and male names. That is what their media release is based on, and their main text makes a ‘prediction graph’ out of that interaction term.

What is completely undiscussed in the main text of the article however is that the interaction with the minimum air pressure goes the opposite way: the lower the air pressure, the lower the death toll from a more feminine-named hurricane! So if the authors had made a ‘prediction graph’ showing the predicted death toll for more feminine hurricanes when the hurricanes had lower or higher air pressures, they would have shown that the worse the hurricane, the lower the death toll if the hurricane had a female name!

The editors and the referee were thus completely asleep for this pretty blatant act of deception-by-statistics. Apparently, one can hoodwink the editors of PNAS by combining the following tricks: add correlated interaction terms to a regression of which one discusses only the coefficients that fit the story one wants to sell; then make a separate graph out of the parameter one needs in the main text, whilst putting technically sounding information in parentheses to throw editors, reviewers, and readers off the scent.

And the hoodwinking in this case is not small either. In order to accentuate what really is a non-result, the authors in the main text claim that “changing a severe hurricane’s name from Charley (MFI=2.889, 14.87 deaths) to Eloise (MFI=8.944,41.45 deaths) could nearly triple its death toll.” This, whilst in the years since 1979 the average death toll for their included hurricanes is 16 for both ‘female hurricanes’ and 16 for ‘male hurricanes’ (own calculations)! The authors conveniently forgot to mention in their dramatic result that Charley would have had to have been a hurricane that did immense economic damage but that had a very high minimum air pressure, ie was actually a very weak hurricane. Only for such an ‘impossible hurricane’ would their own model predict the increase in deaths from a female name. Put differently, I could have claimed that if the hurricane was very strong in terms of low air pressure, that changing the name from Charley to Eloise would have halved the death toll!

The authors also quite willingly pretend to have found things they have not in fact researched. They thus write “”Feminine-named hurricanes (vs. masculine-named hurricanes) cause significantly more deaths, apparently because they lead to a lower perceived risk and consequently less preparedness”” and the conclusions even speak of “gender biases”! Where do they try and measure this supposed bias in actual preparations? You guessed it, nowhere. PNAS should really clean up its act and not allow this sort of article, with its fairly blatant statistical artefacts, to slip through the cracks.

Let me explain the trickery in a bit more depth for the interested reader: air pressure and economic damage are highly related (the correlation is apparently -0.56), which means that one gets a strongly significant interaction between femininity and economic damage only because one simultaneously has added the interaction with minimum air pressure. One then talks about the interaction that goes the way one wants and happily neglects to mention the other one. And one needs both interactions at the same time to get the desired result on the interaction between the names and economic damage: without this interaction with minimum air pressure, what you get is a whole shift upwards of the male death prediction and a loss of significance on the interaction term with economic damage. You see this in the ‘additional analyses’ run by the author, in very small font after the conclusions, wherein the whole thing becomes insignificant for the first period and the reduced coefficient for the later period on the interaction with air pressure coincides with a halving of the coefficient on the interaction with economic damage as well. Hence, without including both interactions you would probably get that the female hurricanes are predicted to be less deadly than the male ones when the economic damage is small and more deadly when the damage is large (to an insignificant extent). So you need the interaction that is almost invisible in the main text and the conclusions to ‘get’ the result that the headlines are based on.

There is another, even more insidious trick played in this article. You see, with only 96 hurricanes to play with, which really only includes 26 to 27 ‘male’ hurricanes, the authors are asking rather a lot from their data in that they want to estimate 5 parameter coefficients, three of which based on names. If you then only use a simple indicator for whether or not a hurricane has a male name, you have the problem that you don’t have enough variation to get significance on anything.

So what did the authors do? Ingeniously, they decided to increase the variation in their names by having people judge just how ‘masculine’ their names were. Hence many of the ‘female’ hurricanes were ‘re-badged’ as ‘somewhat male hurricanes’. So the female hurricanes of the pre 1979 era had an average “masculinity index” of 8.42, whilst those of the new post-1979 era had an average of 9.01. Simply put, according to the authors the female hurricanes ‘of old’, which were of course more deadly as they occurred earlier, were also more masculine, contributing to the headline ‘results’.

Supposedly masculine female names included “Ione”, “Beulah”, and “Babe”. And who judges whether these are masculine names? Why, apparently this was done by 9 ‘independent coders’, by which one presumes the authors meant colleagues sitting in the staff room of their university in 2013! Now, even supposing that they were independent, one cannot help but notice that the coders will have been relatively unaware of the naming conventions in the 1950s and 1960s. How is someone born in 1970 sitting in a staff room in 2013 supposed to judge how ‘masculine’ the name ‘Ione’ was perceived to be in 1950? These older names probably just sounded unusual and hence got rated as ‘more probably male’. Similarly, it is beyond me why ‘Hugo’ would be rated as less masculine than ‘Jerry’ or ‘Juan’.

The authors’ own end-notes called ‘additional analyses’ indeed show that you get insignificant results without this additional variation begotten from making the names continuous. So the authors need to fiddle with the names of the hurricanes, pool two eras together whilst not controlling for era, and add two strongly correlated and opposing interaction terms in the same analyses to get the results they want. It is what economists refer to as ‘torturing the data until it confesses’.

Finally, for the observant, there is the following anomaly telling you something about the judgements made in this research: the masculinity of names is judged on a 1 to 11 scale (only integers) by 9 raters. Yet the averages reported in the authors’ appendices include such values as 1.9444444 (Isaac) and 9.1666666 (Ophelia). Note that if it were true that there were indeed nine raters, then all values should be an exact multiple of one-ninth, ie 0.11111111. The discrepancy indicates that either there were not always nine raters, or else that not all coded values were integers (an impossibility according to the main text). The 9.16666 for instance is a multiple of one-sixth and thus suggests only 6 rates were used for ‘Ophelia’. the 1.9444444 is a multiple of one-eigteenth, suggesting that there were twice as many raters for ‘Isaac’. Alternatively, in both cases, there were nine raters but one of the nine raters picked two values simultaneously (one even and one uneven) and thus added 0.055555 to a multiple of one-ninth in the displayed average. It is not a big thing as this kind of judgement is made all the time but I can’t find the footnote that owns up to this in the paper.

Predictions versus outcomes in 2013?

In the last 5 years, I have made a point of giving clear predictions on complex socio-economic issues. I give predictions partially to improve my own understanding of humanity: nothing sharpens the thoughts as much as having to actually predict something. Another reason is as a means of helping my countries (Australia/the Netherlands) understand the world: predicting socio-economic events is what scientists are for!

Time to have a look at my predictive successes and failures over the last few years, as well as the outstanding predictions yet to be decided. Let us start with what I consider my main failure.

                 Failed predictions

The main area I feel I haven’t read quite right is the conflict in Syria, as part of the general change in the whole Middle East. I am still happy with my long-run predictions for that region, where I have predicted that urbanisation, more education, reduced fertility rates, and a running out of fossil fuels will lead to a normalisation of politics in a few decades time. But at the end of 2012 I was too quick in thinking the Syria conflict was done and dusted. To be fair, I was mainly following the ‘intrade political betting markets’ which was 90% certain Assad would no longer be president by the end of this year, but the prophesised take-over of the country by the Sunni majority has not quite happened. The place has become another Lebanon, with lots of armed groups defending their own turf and making war on the turf of others. The regime no longer controls the whole country, but is still the biggest militia around.

What did I fail to see? I mainly over-estimated the degree to which the West would become involved. I expected the Americans and the Turks to put a lot of resources into the more secular militias, giving them training grounds and more modern equipment. As far as I can tell, this did happen a bit, but simply not to the degree I thought likely, and I don’t really know why. There were several attempts by the US and Turkey to identify an ‘opposition coalition’ to then support, so something hidden from view must have prevented actual support. Perhaps the US has decided it prefers Assad to the alternatives after all.

The willingness of the Iranians and Russians to support the regime has also been stronger than I thought, and the efforts of the Sunni-neighbours to support the non-regime militias have been less cogent than I thought: instead of backing a clear group that had a real future in terms of leading the country (the more secular groups), foreign anti-regime support came mainly for the crazies who went along with the ideology of fanatics elsewhere. That suggests a lack of pragmatic involvement from the neighbours.

I wouldn’t call it a complete predictive failure because Syria as a country no longer exists: it now does have all kinds of regional power brokers and so one could ‘claim’ the regime indeed has lost (most of) its power, but the conflict has gone on longer than the betting markets that I went along with predicted. So this also educates me about the lack of intellectual weight to that kind of political betting market: these are probably more feel-good markets with low turnover that simply don’t aggregate much hidden information. As a related failure, I can mention that I put a low probability on the event that the Muslim brotherhood would overplay its hand when in government in Egypt. I did mention the possibility (see later), but didn’t think it would happen.

 

                Successful predictions

A very recent prediction of mine was on bitcoins. A month ago, I said governments were going to intervene because of the money laundering opportunities in the bitcoin network, and that it hence would not become a dominant trading currency. The next week, the Chinese came down with severe restrictions on bitcoins in their country: financial institutions were not allowed to trade in it and individuals trading in it had to register with their real names, killing off most laundering opportunities. As a result, the value of the bitcoins halved. I wouldn’t claim bitcoins are quite dead yet. It is when many other countries start to enact similar regulation (as some are doing) that it becomes an official curiosum.

Other predictions have been on various aspects of the GFC in Europe. I predicted such things as the Greek defaults when European governments were still pretending they would not occur, the survival of the Euro when there was lots of speculation on imminent euro exits, the inability of the ECB to actually meaningfully monitor banks, and the failure to get agreements on tax evasion (which have all been painfully clear in 2013).  My proudest moment was to predict in December 2011 the overall trajectory of where the politics of the financial crisis was heading: support for weak new institutions in exchange for continued bailouts and forms of money printing, with national sovereignty as the sticking point preventing stronger institutions. We are still on that trajectory now, as this very recent report by the Bruegel Foundation argues which dryly summarises recent events: “Five years of crisis have pushed Europe to take emergency financial measures to cushion the free fall of distressed countries. However, efforts to turn the crisis into a spur for “an ever closer union” have met with political resistance to the surrender of fiscal sovereignty. If such a union remains elusive, a perpetual muddling ahead risks generating economic and political dysfunction.” The latest banking deal fits this mould perfectly.

I am also proud of my predictions on the ill-fated Monti-government in Italy of 2012. Before he was in power, I predicted he was unlikely to have the personality to change anything, and within weeks of him in government (December 2011) I mentioned the reforms he was talking about were dead in the water, months before the magazine The Economist still put him up as a great reformer. Only in 2013 did mainstream media outside of Italy wake up to his failure. I am similarly looking good on my observations regarding the problems in Spain.

On the Middle East, in 2011 I picked the current Lybian chaos coming from its resource curse. A few weeks into the Arab spring I predicted the ensuing grand coalition in 2012 between islamists and the military in Egypt, whereby the islamists would form government but with a tacit agreement with the military not to interfere with the economic interests of that military. I also predicted that the torture machine of the Egyptian military would first deal with the urban youth and then become oriented towards the islamists should they step out of line, which they did.

The main prediction I have been making since 2007 (and which has gotten me into the most trouble!) is the uselessness of looking for a world coalition to reduce CO2 emissions, mainly because the temptation to free-ride is irresistible both within countries and between them. I have thus consistently called to forget about emission strategies and to instead think of technological advances, geo-engineering and adaptation. In each year since 2007, the developments have been accordingly: steady increases in actual emissions with a growing number of scientists and research groups thinking more seriously about geo-engineering: previous agreements on emissions have not been kept and new ones are toothless, whilst you get many beautiful political speeches designed for consumption by the gullible during each new conference on the issues.

In 2013 for instance, the Japanese reneged on their earlier Kyoto promises because they decided to switch from nuclear to fossil, following on from a previous reneging by Canada. Similarly, the EU watered down its commitments in order not to upset the German car industry, whilst China and India and others helped prevent emission agreements with any bite. A nice write-up of the recent Warshaw talk-fest can be found here.  Conspicuous in that write-up is the increased awareness of the importance of adapting to climate change, and the degree to which hope lies with new technology, not massive emission reductions under existing ones. The Australian deal with the EU trading scheme, which was all smoke-and-mirrors anyway, has fallen through, essentially replaced with a policy of ‘business as usual till the bigger players come up with a plan’, which I see as a sensible policy for Australia at the moment.

 

                Predictions on the ledger

In many ways, the ‘emission controls are hopeless’ prediction is a running prediction for decades, so that one is very much still on the ledger. And one in which I am quite willing to bet against those who say they believe serious emission reductions will come about via emission markets or other controls.

Another prediction coming ‘half-good’ recently is the bet with Andrew Leigh on happiness and incomes in rich countries, where my prediction was that richer countries getting even richer would not get happier. For the data we agreed to look at it, this indeed held, but more because I got lucky with the data available – other data showed different results. Read about it in my recent blog on the topic by following the link!

Another prediction ‘on the ledger’ is that there is going to be no real change in Chinese politics till several years after they run out of easy growth opportunities, say 20 years from now. After that, I predict stronger and stronger pressure to adopt a Western-style political system from the Chinese business community. I gave a possible trajectory for how it might happen (local experimentation growing into national systems), but that is not the only way change might happen, if it happens at all. The prediction is the consolidation of the one-party rule till years after the growth has levelled off. That consolidation has indeed been in full swing this last year: as a recent piece of the Institute of Peace and Conflict Studies argues, in 2013 we got more media control and control over the economy by the party. Still, there are some embryonic signs of attempts to get some kind of separation of powers in that country, such as via more independent judiciary and financial institutions.

The prediction that the ‘behavioural genetics’ crowd is going nowhere soon is also a prediction ‘on the ledger’. The same goes for the prediction that Australia is not going to seriously improve its education-for-the-masses anytime soon, and the unlikelihood of solar replacing fossil fuel for mass electricity-generation anytime soon.

There is then a whole heap of predictions that I am quite happy to say have come true, but where it is also a certainty someone else would disagree. For instance, I predicted that the Melbourne Model, which is a change in how the University of Melbourne structures undergraduate education, would lead to dumbed-down degrees. Everything I hear about that place confirms it, but I would be astounded if the chancellery of the University of Melbourne would agree with that assessment! Similarly, my stated fears regarding the Gonski reforms (not quite predictions as I made it clear I had a hard time finding out what was actually going to happen) are looking all-too-true, but I am sure the ministries involved would disagree. One can trawl my archives for several more such ‘debatable’ prediction outcomes.

Finally, I have a bet on with Conrad Perry for what is going to happen in Egypt next. My prediction is that the next elected government will again be an islamist-lead government, a kind of Brotherhood 2.0. They may change labels and be even more careful, but I thought it likely that they would be involved as a dominant player in the next elections simply because of the high level of religiosity in that country. Conrad Perry bets on ‘all other outcomes’ with a bottle of red to the winner. Jim Rose also made an implicit prediction, which is that the new generation of military are going to be successful in their bid to monopolise power in Egypt, but he didn’t bet anything. Still, Jim is looking rosy on that prediction.

The prediction+bet with Conrad on Egypt was entered into around August/September and things have moved on a bit since then. The Egyptian military has proven more popular and bent on total control than I thought, but we are still looking at a situation in which one is likely to get democratic elections (though the military might well rig them). I will say I am less confident about my prediction now than 3 months ago, essentially because the military has been more brutal than I thought they would be, but there is still a chance for my prediction to happen so I am not ready to concede defeat on that one yet!

The Xmas quiz answers and discussion

Last Monday I posted 4 questions to see who thought like a classic utilitarian and who adhered to a wider notion of ethics, suspecting that in the end we all subscribe to ‘more’ than classical utilitarianism. There are hence no ‘right’ answers, merely classic utilitarian ones and other ones.

The first question was to whom we should allocate a scarce supply of donor organs. Let us first briefly discuss the policy reality and then the classic utilitarian approach.

The policy reality is murky. Australia has guidelines on this that advocate taking various factors into account, including the expected benefit to the organ recipient (relevant to the utilitarian) but also the time spent on the waiting list (not so relevant). Because organs deteriorate quickly once removed, there are furthermore a lot of incidental factors important, such as which potential recipient is answering the phone (relevant to a utilitarian)? In terms of priorities though, the guidelines supposedly take no account of “race, religion, gender, social status, disability or age – unless age is relevant to the organ matching criteria.” To the utilitarian this form of equity is in fact inequity: the utilitarian does not care who receives an extra year of happy life, but by caring about the total number of additional happy years, the utilitarian would use any information that predicts those additional happy years, including race and gender.

In other countries, the practices vary. In some countries the allocation is more or less on the basis of expected benefit and in the other is it all about ‘medical criteria’ which in reality include the possibility that donor organs go to people with a high probability of a successful transplant but a very low number of expected additional years. Some leave the decision entirely up to individual doctors and hospitals, putting huge discretion on the side of an individual doctor, which raises the fear that their allocation is not purely on the grounds of societal gain.

What would the classic utilitarian do? Allocate organs where there is the highest expected number of additional happy lives. This thus involves a judgement on who is going to live long and who is going to live happy. Such things are not knowable with certainty, so a utilitarian would turn to statistical predictors of both, using whatever indicator could be administrated.

As to length of life, we generally know that rich young women have the highest life expectancy. And amongst rich young women in the West, white/Asian rich young women live even longer. According to some studies in the US, the difference with other ethnic groups (Black) can be up to 10 years (see the research links in this wikipedia page on the issue). As to whom is happy, again the general finding is that rich women are amongst the happiest groups. Hence the classic utilitarian would want to allocate the organs to rich white/Asian young women.I should note that the classic utilitarian would thus have no qualms about ending up with a policy that violates the anti-discrimination laws of many societies. Our societies shy away from using observable vague characteristics as information to base allocations on, which implicitly means that the years of life of some groups are weighed higher than the years of life of another. The example thus points to a real tension between on the one hand classic utilitarianism and its acceptance of statistical discrimination on the basis of gender and perceived ethnicity and on the other hand the dominant moral positions within our society. Again, I have no wish to say which one is ‘right’ but merely note the discrepancy. As to myself, I have no problem with the idea that priority in donor organs should be given to young women though I also see a utilitarian argument for a bit of positive discrimination in terms of a blind eye to ethnicity (ie, there is utilitarian value in maintaining the idea that allocations should not be on the basis of perceived ethnicity, even though in this case that comes at a clear loss of expected life years).

The second question surrounded the willingness to pre-emptively kill off threats to the lives of others.

The policy reality here is, again, murky. In order to get a conviction on the basis of ‘attempted’ acts of terrorism or murder, the police would have to have pretty strong evidence of a high probability that the acts were truly going to happen. A 1-in-a-million chance of perpetrating an act that would cost a million lives would certainly not be enough. Likely, not even a 10% chance would be enough, even though the expected costs of a 10% chance would be 100,000 lives, far outweighing the life of the one person (and I know that the example is somewhat artificial!).

When it concerns things like the drone-program of the west though, under which the US, with help from its allies (including Australia), kills off potential terrorist threats and accepts the possibility of collateral damage, the implicit accepted burden of proof seems much lower. I am not saying this as a form of endorsement, but simply stating what seems to go on. Given the lack of public scrutiny it is really hard to know just how much lower the burden of proof is and where in fact the information is coming from to identify targets, but being a member of a declared terrorist organisation seems to be enough cause, even if the person involved hasn’t yet harmed anybody. Now, it is easy to be holier-than-thou and dismissive about this kind of program, but the reality is that this program is supported by our populations: the major political parties go along with this, both in the US and here (we are not abandoning our strategic alliance over it with the Americans, are we, nor denying them airspace?), implying that the drone program happens, de facto, with our society’s blessing, even if some of us as individuals have mixed feelings about it. So the drone program is a form of pre-emptively killing off potential enemies because of a perceived probability of harm. The cut-off point on the probability is not known, but it is clearly lower than used in criminal cases inside our countries.

To the classic utilitarian, if all one knew would be the odds of damage and the extent of damage, then the utilitarian would want to kill off anyone who represented a net expected loss. Hence the classic utilitarian would indeed accept any odds just above 1 in a million when the threat is to a million lives: the life of the potential terrorist is worth the expected costs of his possible actions (which is one life). If one starts to include the notion that our societies derive benefit from the social norm that strong proof of intended harm is needed before killing anyone, then even the classic utilitarian would increase the threshold odds to reflect the disutility of being seen to harm those social norms, though the classic utilitarian would quickly reduce the thresholds if there were many threats and hence the usefulness of the social norm became less and less relevant. To some extent, this is exactly how our society functions: in a state of emergency or war, the burden of proof required to shoot a potential enemy drastically reduces as the regular rule of law and ‘innocent till proven guilty’ norms give way to a more radical ‘shoot now, agonize later’ mentality. If you like, we have recognised mechanisms for ridding ourselves of the social norm of a high burden of proof when the occasion calls for it.

As to personally pulling the trigger, the question to a utilitarian becomes entirely one of selfishness versus the public good and thus dependent on the personal pain of the person who would have to pull the trigger. To the utilitarian person who is completely selfless but who experiences great personal pain from pulling the trigger, the threshold probability becomes 2 in a million (ie, his own life and that of the potential terrorist), but to a more selfish person the threshold could rise very high such that even with certainty the person is not willing to kill someone else to save a million others. That might be noble under some moral codes, but to a utilitarian it would represent extreme selfishness.

So the example once again shows the gulf between how our societies normally function when it concerns small probabilities of large damages, and what the classic utilitarian would do. A utilitarian is happy to act on small probabilities, though of course eager to purchase more information if the possibility is there. Our societies are less trigger-happy. Only in cases whereby there is actual experienced turmoil and damage, do our societies gradually revert to a situation where it indeed just takes a cost-benefit frame of mind and suspends other social norms. A classic utilitarian is thus much more pro-active and willing to act on imperfect information than is normal in our societies.

The third question was about divulging information that would cause hurt but that did not lead to changes in outcomes. In the case of the hypothetical, the information was about the treatment of pets. To the classic utilitarian, this one is easy: information itself is not a final outcome and, since the hypothetical was set up in that way, the choice was between a lower state of utility with more information, versus a higher state of utility with less information. The classic utilitarian would chose the higher utility and not make the information available.

The policy reality in this case is debatable. One might argue that the hypothetical, ie that more information would not lead to changes but merely to hurt, is so unrealistic that it basically does not resemble any real policies. Some commentators made that argument, saying they essentially had no idea what I was asking, and I am sympathetic to it.

The closest one comes to the hypothetical it is the phenomenon of general flattery, such as where populations tell themselves they are god’s chosen people with a divine mission, or where whole populations buy into the idea that no-one is to blame for their individual bad choices (like their smoking choices). One might see the widespread phenomenon of keeping quiet when others are enjoying flattery as a form of suppressing information that merely hurts and would have no effect. Hence one could say that ‘good manners’ and ‘tact’ are in essence about keeping information hidden that hurts others. Personally, though I hate condoning the suppression of truth for any cause, I have to concede the utilitarian case for it.

The fourth and final question is perhaps the most glaring example of a difference between policy reality and classic utilitarianism, as it is about the distinction between an identified saved life and a statistically saved life. As one commenter already noted (Ken), politicians find it expedient to go for the identified life rather than the un-identified statistical life, and this relates to the lack of reflection amongst the population.

To the classic utilitarian, it should not matter whose life is saved: all saved lives are to the classic utilitarian ‘statistical’. Indeed, it is a key part of utilitarianism that there is no innate superiority of this person over that one. Hence, the classic utilitarian would value an identified life equally to a statistical one and would thus be willing to pour the same resources into preventing the loss of a life (via inoculations, safe road construction, etc.) as into saving a particular known individual.

The policy practice is miles apart from classic utilitarianism, not just in Australia but throughout the Western world. For statistical lives, the Australian government more or less uses the rule of thumb that it is willing to spend some 50,000 dollars per additional happy year. This is roughly the cut-off point for new medicines onto the Pharmaceutical benefit Scheme. It is also pretty much the cut-off point in other Western countries for medicines (as a rule of thumb, governments are willing to pay about a median income for another year of happy life of one of their citizens).

For identified lives, the willingness to pay is easily ten times this amount. Australia thus has a ‘Life Saving Drugs’ program for rare life-threatening conditions. This includes diseases like Gaucher Disease, Fabry disease, and the disease of Pompe. Openly-available estimates of the implied cost of a life vary and it is hard to track down the exact prices, but each year of treatment for a Pompe patient was said, in a Canadian conference for instance, to cost about 500,000 dollars. In New Zealand, the same cost of 500,000 is being used in their media. Here in Australia, the treatment involved became available in 2008 and I understand it indeed costs about 500,000 per patient per year. There will be around 500 patients born with Pompe on this program in Australia (inferred from the prevalence statistics). Note that this treatment cost does not in fact mean the difference between life and death: rather it means the difference between a shorter life and a longer one. Hence the cost per year of life saved is actually quite a bit higher than 500,000 for this disease.

What does this mean? It means, quite simply, that in stead of saving one person with the disease of Pompe, one could save at least 10 others. In order for the person born with Pompe to live, 10 others in his society die. It is a brutal reality that is difficult to talk about, but that does not change the reality. Why is the price so high? Because the pharmaceutical companies can successfully bargain with governments for an extremely high price on these visible lives saved. They hold politicians to ransom over it, successfully in the case of Australia.

Saving one identified life rather than ten unidentified ones is not merely non-utilitarian. It also vastly distorts incentives. It distorts the incentives for researchers and pharmaceutical companies away from finding solutions to the illnesses had by the anonymous many, to finding improvements in the lives of the identifiable few. It creates incentives to find distinctions between patients so that new ‘small niches’ of identified patients can be found out of which to make a lot of money. Why bother trying to find cures for malaria and cancer when it is so much more lucrative to find a drug that saves a small but identifiable fraction of the population of a rich country?

So kudos to those willing to say they would go for the institution that saved the most lives. I agree with you, but your society, as witnessed by its actions, does not yet agree, opening the question what can be done to more rationally decide on such matters.

Thanks to everyone who participated in the quiz and merry X-mas!