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!

Rich countries and happiness: the story of a bet.

Do countries that are already rich become even happier when they become yet richer? This was the essential question on which I entered a gentleman’s bet in 2004 with Andrew Leigh and which just recently got settled.

The reason for the bet was a famous hypothesis in happiness research called the Easterlin hypothesis which held that happiness did not increase when rich countries became even richer. When I was preparing a presentation on this matter in 2004 I used the following graph to illustrate the happiness income relation across countries:

gruen 2004 image

This graph shows you the relation between average income (GDP in purchasing power terms) and average happiness on a 0-10 scales for many countries. As one can see, the relation between income and happiness is upward sloping for low levels of income, but becomes somewhat flat after 15,000 dollars per person. I championed the idea that this was not just true if you looked across countries, but that this would also hold true over time.

Andrew Leigh’s thinking was influenced by other data, particularly a paper by Stevenson and Wolfers which – he thinks debunks the Easterlin hypothesis. Here’s one of their graphs:

 

Wolfers2008

What’s striking about this graph is that the dotted line slopes up in the top right corner. In other words, the relationship between happiness and income becomes stronger, not weaker, for countries with average incomes over $15,000. Andrew thinks that this is because they specify income in log terms (in other words, we’re looking at the effect on happiness of a percentage increase in income rather than a dollar increase in income). I think it’s because the Gallup poll isn’t measuring happiness, but is instead asking people to rank themselves on the Cantrill ladder of life scale.

So our gentleman’s bet was in effect a bet on whether happiness in the world value surveys behaved different to the ladder question of the Gallup polls, and on whether the short-run relation between income and happiness was strong enough to show up in periods of 5 to 10 years as well. Andrew thought it would, I thought 5-10 years would be long enough for the typical long-run no-effects findings to show up and that happiness has a different relation with income than the Cantril-question. So we bet on whether one would get a significantly positive relation between GDP growth and happiness changes for the rich countries when one looked at the World Value data for 2005. We agreed to look at the relation between income and happiness using country-average variation. The winner would get 100 bucks.

Now, both of us forgot about the bet for a few years as the data was supposed to become available. Only recently did Andrew remind me of our bet and asked to check what had happened.

When I (with research assistance from Debayan Pakrashi) started to look into this data again, it quickly became apparent that Andrew and I had been pretty sloppy in formulating the precise conditions of the bet. In many ways, our bet had been far too vague.

For one, the World Value survey is not in fact held in particular years. Rather, some survey is run almost every year in some country that adds to the collection of surveys known as the World Value Survey. Hence there was really no such thing as a ‘2005 wave’. Taken literally, only Australia, Finland, and Japan had a survey in 2005 and were countries that in the previous wave already had a GDP of 15,000 dollars. In all those countries, income had gone up a lot since their previous survey, with Australian happiness down and Japanese and Finnish happiness up. That is a bit meagre as ‘waves’ go.

So the first ‘addition’ was to have a bandwidth of years for the ‘2005’ waves that included 2004, 2005, 2006, 2007, and 2008. That gave 12 countries that were rich enough in the previous wave to qualify. The raw data was:

Table1_2013

The next ‘snag’ was of course that there are many ways to define the dependence on income: linear or logarithmic. With logarithmic income one normally gets stronger statistical significance on income, so we went for logarithms.

Then, of course, there are still many other things one can put into the regression. Does one account for effects of particular years (in bands) and for the level of happiness that a country starts? We decided to try it all. Hence the final ‘deciding’ set of regressions were as follows:

 

Table4_2013

Which tells you that the relation between income changes and happiness changes (the last two columns) was either quite insignificantly positive or even negative if one entered year-bands.

When one reflects on the list of countries used in the analysis though, it is clear that the outcome of the bet will have had little to do with the true relation between income and happiness. It will have hinged on hidden aspects of the data. For instance, the Australian world value survey in 1995 was run differently from the 2005 version. Hence the big drop in Australian happiness you see in this period for this data does in fact not show up for other Australian data (like the HILDA). So one suspects some change in the data-gathering to be responsible for it. Indeed, the level of Australian happiness in this data is markedly below the level found for the HILDA (where it is almost 8.0).

Similarly, the big increase in Japanese happiness in this period doesn’t show up either in other Japanese data and so probably has something to do with changes in how the survey was run there. The changes can relate to the months in which the surveys were held, the precise words used for the happiness question, the questions preceding the happiness questions, the cities in which the survey was run, how the survey was run (face-to-face or via telephone), etc.

So I may have gotten lucky and won the bet, but one cannot see the outcome as decisive evidence that income and happiness have no long-run relation within rich countries. The data for the 2010 post-GFC wave might well show the opposite!

A fable of Eunuchs, Praetorians, and University funding cuts.

Imagine yourself to be in the mythical Land of Beyond where you need minions to do a dirty job that men with honour would refuse to do. A classic trick in this situation is to pick people despised by the rest of society who are thus dependent on protection and will simply do what is asked for.

The Chinese emperors hit upon this truth when they started to surround themselves with eunuchs, despised by the rest of Chinese society and thus fiercely loyal to their protector, the Emperor. The roman emperors, similarly, made a habit of surrounding themselves with freed slaved who were despised by other Romans, as well as by a dedicated palace guard (the Praetorians) who were the only militia allowed in the vicinity of Rome.

The European colonialists too used this basic ‘dirty dozen’ technique when it came to keeping a large population in check with minimal own presence, particularly in Africa, by elevating some small despised group (ethnic or religious minorities) as the preferred club from whom the senior administrators came. This small favoured group would get personal benefits (riches and influence) but in return they would do whatever the colonizers wanted.

To see the relevance of this for university cuts in the Land of Beyond, you first need to step back a level and imagine yourself to be the Vice Chancellor of a second-rate university that brings in, say, a billion ‘Beyond’ dollars a year out of which some 300 million is money you dont really need to generate that 1 billion. It is ‘potential profit’ if you like.

Now, your first thought will of course be to give as much of this money to yourself as you can. That is not so easy though: in Beyond, universities are non-profit organisations nominally run by senates and full of academics who like to monitor and criticise you. You would never get away with giving yourself multi-million dollar salaries and huge offices if academics are really watching your every step.

So in order to get more of the profit, you need to subdue two groups, the academics and the senate. You subdue the academics by keeping them busy with ‘compliance’ and having a lot of systems in place to punish them if they become pesky. You thus include in your rules that anything that harms the reputation of the university is a sacking offence. You put yourself at the top of the committees that decide on professorial promotions and academic bonuses so that you are their direct boss. You appoint hundreds of administrators to monitor the media, teaching, and student-related activities of the academics with the purpose of keeping them quiet and punishing them when they get out of line.
You subdue the senate by overloading them with information (for which you need again more administrators) and by keeping them happy with luxuries and gifts. Over time, you attempt to get control of the mechanism via which new members get to be in these senates.

Now, the essential problem you face in this as a VC is how to ensure that the people helping you with your take-over plans are somewhat loyal to you rather than to something as silly as the goals of the university or academia or even to the needs of Beyond. It is loyalty to yourself that you need in order to eventually be able to get away with giving yourself huge amounts of money.

You remember your history lessons and realise that what you need is a set of eunuchs: people despised by the academics in your organisation who will thus have the same incentive as you have to subdue the academics and grab as much of the university resources as possible.

What are the equivalent of eunuchs in universities? Why, non-academics of course! Better still, non-academics whom you give academic titles for they will be even more despised! Hence you pick the most efficient bullies you can find, call them all professor and put them in charge of the divisions that subdue the academics and that send mountains of information to the university senate to ensure they will just go along with whatever you happen to ask of them at the end of some sumptuous occasion.

Due to your brilliance and foresight, the trick works like a charm and you find yourself earning well over a million, with several huge offices, and in a position to bargain for even more kick-backs from outsiders who want to use parts of the university for their own end (property developers and the like).

Now imagine yourself in the layer yet higher: you are now an ambitious paymaster in the Capital of Beyond, someone who nurtures a reputation for being able to get things done even if they might not really be in Beyond’s best interests. You too have a control problem for you want all kinds of things from universities. You would like the universities to keep the population happy by churning out cheap degrees to domestics. You also want universities to sell visas to smart oversees students by means of high fees for almost no education (cross-subsidising those domestics). Basically, you want universities to abide by whatever fancy drifts into the head of your current minister.

The control problem you have as a ‘wheeling and dealing’ senior civil servant in Beyond is again those pesky academics: they are self-righteous, not all that interested in your opinion or even your money, and wouldn’t easily go along with these plans. They might well flatly refuse to sell visas to foreigners because they would baulk at short-changing the education given to those foreigners. Indeed, they would probably laugh in your face if you suggested that universities should fall in line with, say, your wish to have a campus in the middle of nowhere just because it is a marginal constituency.

Just imagine what confident academics would do if you told them to cut their budget by 900 million! Why, they might do something as bold and brash as to honestly tell their students that there are no funds to properly educate them. Imagine the political fallout of such honesty by a bunch of self-righteous academics who won’t simply do your bidding! No no, it is quite clear to you that the last people you want leading universities are academics. You want leaders who know what you really mean when you talk about ‘university accountability’, ‘stakeholder management’, ‘strategic visions’ and ‘preparing for the future’.

So the senior Beyond bureaucrat too finds herself in the situation of needing eunuchs in charge of universities. You don’t mind if they get some private benefits out of the arrangement as long as they do your bidding and not rock the boat politically.

Now think a step higher again and consider why Beyond might have fixers at the top of the ministries …..

 

Paul Milgrom’s 65th Birthday

My PhD advisor turns 65 today and here, at Stanford, we are having a conference in his honor. I made some remarks that I thought I’d post here.

I am here to talk about Paul’s contributions to applied theory. While Susan and Yeon-Koo have talked about theoretical contributions that so many in this room associate with Paul, to the wider profession, his main contribution is somewhat different.

Take a look here at his most highly cited work. With just a couple of exceptions, it is all applied theory. And moreover, when you look at where those citations are coming from it is not economics. It is management, strategy and finance. In other words, Paul is the most significant theorist in business and management, today, and possibly ever.

How did this happen? To give some context, there is really a schism in economics and social science in general. It surrounds the issue of complexity. There are many social scientists who think the world is too complex to make simplified theory useful. When you use specific assumptions — like rationality or expected utility or equilibrium — or more commonly in applied work, functional forms — they argue that you miss so much that what remains is useless.

Paul’s view of the world, it seems to me, is that complexity must be respected but our tools of economic theory can guide us as to their own appropriateness. In that respect, simplicity is a virtue and is manageable so long as the tools and methodology applied is understood.

Take the famous result in agency theory of Bengt and Paul’s that simple wage functions can be optimal. Everyone knew those functions were employed in practice but the ‘informal’ reaction was that it was a response to difficult of doing more, or a saving of cognitive costs or a lack of skill. Paul said no, it can’t be that. There will always be a smart agent who would improve it and then we would see heterogeneity. Instead, the complexity of the world itself would give rise to a simple response.

That is one way to read all of Paul’s work. Simplicity must be a response to the complex environment. And simple theoretical treatments can be immediately generalised if those treatments capture key trade-offs. Paul taught us where to look.

What I learned from this is that in applied theory there is a symbiotic relationship between the real world phenomenon, the formal model and its intuition. And there are feedbacks between all three in the exploration that is economic theory. I had the pleasure of observing Paul, and John, during the hey-day of their foray into organisational economics. Time and time again, they would take an individual transaction (Paul contracting with a builder, say; Paul thinking about spectrum packets; observations of a Toyota factory in Japan) and realise why existing theory just couldn’t apply. In that process, they would identify and relax the key assumption and draw new implications (the job design should change; you have to us computer technology to deal with substitutes and complements in packages; that change will be hard) and discover it in the real world. They would leave behind a framework for empirical researchers to follow and that is where all those citations come from. They seep through MBA curriculum. It is a tremendous legacy.

Not only that, Paul appears to yearn for ‘beauty’ in his theories. If it is a mess, you must be missing something. You haven’t identified the key trade-offs. These papers are beautiful. I have taken this to my own applied work. Avoid contrivance. Understand intuition. And above all, become a useful theorist. That will make theory useful.

Now for we mortals this is a challenge. Paul lights the path because it comes easily to him. I remember him lamenting to me that it took him a whole day — a whole day — to get the model right for a paper. But that doesn’t mean that we should not aspire for the same.

Applied theory is an area that continues to have issues finding its place in economic research. But Paul has, in many respects, allowed good applied theory to flourish and rise to a new standard.

In conjunction with the conference, everyone involved contributed to his Wikipedia page as a birthday gift. Suffice it to say, this was greeted with enthusiasm and it produced one of the most comprehensive entries of any economist (and perhaps the longest, in the terms of bytes, of them all). Several Nobel prize winners contributed so I think it is safe to say that quality is high. Here is Al Roth’s account.

Paul Milgrom Wikipedia page.April 19 2013

The Role of Research in Business Schools

In the Financial Times, there was a feature piece interviewing Larry Zicklin who wants to eliminate research funding and promotions for academics in business schools. Naturally, I disagree. I wasn’t the only one. UTS’s Timothy Devinney published a comment on that post that he gave me permission to reproduce here.

Comment by Professor Timothy Devinney:

It is interesting how over my 20 years as an academic I have heard this sort of logic again and again and again. Invariably it is from adjunct faculty with a more ‘professional’ background complaining that they do not understand what it is that academics do and why the do not ‘teach’ more or that their promotions should be based more on teaching. Unfortunately such arguments, while valid to the individuals who make them, are based mainly on faulty logic and a basic misunderstanding of what is going on. For example, whenever I go and work with a company I am amazed at how much time managers waste actually doing nothing but monitoring and interacting with other managers? Why are they not working with customers more? Why are they not out in the field rounding up more business? Isn’t it inefficient to have them in meetings so often invariably doing little more than playing power games against other managers? Of course, this is a naive viewpoint and it is based on my failure to understand what these managers do. Ditto Mr. Zicklin’s view of academics in business schools. Here are some points that matter.

His view of teaching is dominantly one of information dissemination. Having been at the top and bottom of the academic food chain (being both at U. Chicago and now in Australia at what is dominantly a teaching factory) I have seen the differences. The students at Chicago get knowledge at the coal face by people who understand what is both leading edge and sophisticated. Students here get commoditized information delivered by individuals who only know what they read because they are not leading edge scholars. Indeed, where the MOOC Tsunami will hit is on this commoditized end of the business.

Second, his viewpoint is based on the ‘leach on society’ view of academics. I argue that good scholars are some of the most entrepreneurial people in the world. Imagine Mr. Zicklin working in a business in which the failure rate is > 90% (which is the rejection rate of most leading journals). Also, it does not matter where you reside or which university you are at since the rejection is based on blind review. Imagine your typical corporate manager working in an environment in which their work was evaluated blindly and in 9 cases out of 10 rejected as being inadequate. Imagine also those individuals attempting to run projects on little more than scraps of funding (for an average academic on what is known as a 40:40:20 contract the actual cost of the research per year amounts to only about $50,000 per year). Most companies spend more on business class airfare for managers than this. Most universities spend 20 times this on the basketball coach.

Third, most good academics could easily make more money outside academics than inside academics. When I received my PhD I had an offer from one of the major consultancies. It was three times my academic salary. But I remained an academic because I believed in what I wanted to do. I argue that the difference between managers and academics is that managers give up what they love for money while academics give up money for what they love. If you take away the scholarship aspect of this then the equation skews toward money. So if I am going to sing for my supper then I want to be paid for singing. Unfortunately as soon as that occurs I end up choosing not to be an academic. In reality, we have serious problems getting good brains to commit to getting phds and hence the pool of potential future faculty is actually drying up. If anything the premium needs to be bigger not smaller.

Fourth, Mr. Zicklin’s argument that promotion is all about research and not teaching is just wrong. You cannot get promoted anywhere as a basket case in the classroom. Indeed, nearly every academic I know is quite good to very exceptional in the classroom. It is also the cases that I know where we looked at exactly this we found that our best scholars were our best teachers. So this idea that there are ‘teachers’ and there are ‘researchers’ is just nonsense. The best scholars are on average exceptional at communicating. Mr. Zicklin’s problem is that he is basing his viewpoint on myth and exceptions and not evidence. However, in the end, if your best scholars are you best teachers the institution must make a decision as to the allocation of their time. Unfortunately, good scholars are rare and institutions cannot replace them as easily as they could to one trick teaching ponies.

Finally, the fact that academic journals are not read by managers is absolutely meaningless. These journals are not meant for managers. That is why you have HBR, Sloan Mgt Review, McKinsey Quarterly and other outlets. Any good journalist or writer will tell you that you write to the audience. If you want to communicate with managers you do it differently than when you speak to other scientists. As soon as you attempt to write to everyone you actually communicate with no one. I personally am the sort of academic that communicates to broad audiences (like my colleague Pankaj) but I do not expect managers to read my academic articles. Also, in a response to Freek Vermeulen on this same topic (also in the FT), I argued that we as academics influence practice one student at a time by how we do what we do and what we pick to have in our classes and how we communicate in public forums. Many of the examples above are good examples of others. And there are many many more.

So while Mr. Zicklin’s arguments appear to be logical and reasonable I would argue that you need to be careful about what you wish for. There is more than one tsunami approaching and my view is that the more dangerous one is that there are fewer and fewer potential scholars choosing to be academics because the personal benefits of such a career are being eroded while the financial compensation is not sufficient to offset this. If I had to make the decision today that I made 20+ years ago I would not go into academics. I would chase the money, cash out and then become and adjunct faculty member writing opinion pieces for the FT while living the life of the casual academic.

Are there unhelpful mathematical models of economic phenomena?

Take your bog-standard first-year economics story of why money (sea shells, coins, notes, bank statements) exist. Money, you will be told, is a means of exchange, a store of value, and a unit of accounting, thoughts going back to David Hume (18th century) and earlier.

When explaining the idea of exchange to students you say things like ‘you can’t exchange a hundredth of a sheep for a loaf of bread so you want something to represent the value of a hundredth of a sheep, and in any case it’s a long slog to the market carrying a sheep around’.

When explaining the idea of a store of value you say things like ‘You would like to be able to consume things when you are old without working when you are old. That means you need to save up wealth in the form of something that doesn’t perish. Sheep perish, gold does not’; and when explaining the unit of value idea you say things like ‘we all think of the value of things in terms of a numeraire, such as that milk costs 1 dollar per liter and flour 2 dollars a kilo. None of us think in terms of 1 liter of milk being worth half a kilo of flour. Given many different products, it is more convenient to think of the value of each of them in terms of something you can compare across these goods. Money performs that role and you will find that even when the unit of money changes (such as moves from the Deutschmark to the Euro) that people will continue to calculate everything back in terms of the old money for many years’.
Simple stories, no? And most students will ‘get the point’ of each of these three stories. They will see the difficulties of exchange with lumpy goods that cannot easily be stored and exchanged, and they will see the point of being able to save up for a later date and that requires some form of storable money.

Simple though these arguments are, you will be hard-pressed to find mathematical models of them that anyone would recognise as remotely capturing these verbal arguments. It tells you something about the limits of mathematical models to think through why recognisable models of money do not exist. So bear with me as I take you through the actual difficulties of modelling money and how those difficulties end up as unhelpful advise from theoretical economists to policy makers.Think of the actual difficulties involved in modelling the story of money as a medium of exchange. Before even thinking about money, you have to start from a model with exchange. This means you need to model the production of more than one good and you must build in a reason, like comparative advantage, why individuals do not simply produce all the goods they need by themselves. For realism you would want the goods to be lumpy, perishable, and to require long-term investments. After all, sheep herding and crop-growing do not happen overnight and neither sheep nor apples can meaningfully be stored for very long or exchanged in halves.
You immediately hit your first mathematical snag right there: if production is lumpy (you can’t produce half-apples), then you won’t get the simple outcome that someone will spend all his time on what he is best at. An individual could optimally spend his time by producing one sheep and two apples even though he has a comparative advantage in sheep, simply because he can’t make exactly two sheep. If you want lumpiness in your model, you thus would have to solve the problem of how a person would optimally allocate a fixed amount of time over lumpy investment projects. This is known in the Operations Research literature as the knap-sack problem  (in which you need to decide which lumpy goods to put into a knapsack of particular size) and it is known to be an ‘NP-hard’ problem. Simply put, you know of such problems that there is a single optimal solution but it may take a long time to actually find it. Solving just that knapsack problem for a single individual is already something that may take a computer years if you choose the bundle of potential goods to be large enough, and there will be cases in which you will find that even with comparative advantage the sheep herders may grow enough apples to not need exchange.
How do you solve that snag, which incidentally arises in all models of production? The reality is that you don’t because solving just that one leaves you with a model in which you can solve little else and in which you are not assured of any real impetus for exchange. Hence you ‘simplify reality’. You thus presume that there is no such thing as a lumpy good and that people spend their time producing a ‘continuous’ amount of goods, say, 3.271 sheep or 14.231 apples. Without lumpiness, people will specialise in making one thing and have a reason to trade. Note that you thus have already given up on describing the most intuitive reasons for having money around: you can no longer meaningfully talk about the difficulties of exchanging a hundredth of a sheep for half an apple since you now have presumed a world in which you produce sheep in hundredths and apples in halves.
Moving on, the next modelling problem you hit is that it must be the case that different individuals happen to want what the other produces, a ‘coincidence of wants’. Indeed, you want some kind of place (a market) where people come to exchange what they have produced. In model-land you must answer every counter-factual. You must thus have a reason why traders would use money instead of giving each other credit or just exchanging bundles of good (since goods are now not lumpy, you can just go to the market with your 2/3 sheep and exchange it in one big free-for-all for all the goods you need). Such thoughts may sound absurd to you, but working them through has occupied really good mathematicians for years. It is in fact nigh impossible to solve models in which people do not know exactly beforehand what will happen in a market.

You see, as soon as you say that a person does not know beforehand what other people have produced and at what prices they might trade, you are in the world of limited information and in the world where it is possible that people make mistakes (go to the market empty handed, produce the wrong things, etc.). You are then in the business of having to specify how people form expectations about what others would do and what prices they would trade at.

You are then also in the business of working out whether there are perhaps multiple equilibria (i.e. different configurations of the whole economy) and the issue of how people who don’t know each other could actually coordinate on a particular configuration. You then for instance have to contend with the possibility that nobody shows up at the market because they expect nobody else to show up. You have to contend with the possibility that you get the wrong prices, under which there is no specialisation at all.

You have to contend with the problem that the only people to show up wouldn’t want to trade with each other because they have produced the same thing and you have to figure out how a group of people would actually arrive at a price (or prices). Each of these sub-problems is considered exceptionally hard by theorists: only under very specific mathematical assumptions can you be absolutely guaranteed that the problems above do not occur.
Hence, what do you do? Well, again, the reality is that you assume away all these problems. You simply make those assumptions that guarantee you that everyone who produces something is ‘magically’ matched up with someone else who has something they want to trade with. Also, you now presume the existence of some kind of all-powerful benevolent entity, say god. You need such an entity to do away with elements in your model you cannot model but need anyway, such as how prices arise before any exchange takes place (if prices change during exchange one gets into exceptionally complicated dynamics where you need to start talking about the expectations that people have of possible price paths). So you invent a god that takes care of such issues. God, in his first incarnation as a Walrasian auctioneer, announces the prices at which everyone is willing to trade, whereby everyone believes god and acts accordingly. God, now in his second role as a benevolent and completely trusted government, then also provides a means of exchange that is not perishable, i.e. money.

Usually, a third sleight of hand is needed to get a workable model and that is to have a situation in which there is no such thing as a mistake because there is no such thing as expectations that are incorrect. This of course basically presumes away the original problem you were starting out to model, but that is an almost inevitable casualty of the wish to have a tractable economic model.

What kind of models of money do we end up with? To my taste, the best that mathematicians have come up with is the story that some sheep producers have a craving for eating apples in the night, but they are themselves just innately incapable of producing apples and their sheep always die at the end of the day (i.e. they must be eaten before the end of the day. New ones are only born at the start of the next day). This means that the sheep herder must sell his sheep during the day to the apple maker whilst buying the apples during the night (apples also perish at the end of each half day so he can’t trade during the day). In a modelling sense, that ensures you the ‘coincidence of wants’ you need to have a role for exchange and ensures that sheep herders and apple farmers cannot just trade their produce. By assuming that they not trust each other, but that they do trust the provider of money, you ensure that they do not just trade promises but use money for their trades. Within this kind of basic set-up you can even introduce monetary policy in the form of allowing god to hand out more money to specific groups or to reduce the value of the money in circulation. Whole ‘policy edifices’ have been built upon the basic structure of sheep herders having cravings for apples in the night. For those who are interested, I am talking about the model by Lagos and Wright (2005) and the many extensions on their basic idea.
Now, anyone in his right mind would laugh out loud at the story above as it comes nowhere close to the historical stories told about why we have money and what its role is in the economy: big historical problems in the emergence of money concerned the fact that there was no trusted government, and the value of money had a lot to do with the actual costs of information and transportation, costs that the story talked about above had to assume away. Yet the story of apples and sheep above, believe it or not, is one of the dominant stories told in ‘micro-founded’ monetary economics. It is in that kind of model-economy that they talk about money, credit, banks, regulation, etc. If it weren’t for the fact that it is deemed cutting-edge research, you would have to cry.
I hope you will take my word for it that the problems of generating models in which money exists because of savings and as a numeraire good are equally hard to set up and hence such models don’t exist at all as far as I know.
The value of the actual models of money are mainly as proof of concept, i.e. that you can think of a micro-model in which money emerges and where you can base the emergence of money on at least one of the underlying micro-motivations you think are important for the existence of money (the advantage of having a more varied consumption bundle). It is not the model you would have wanted but at least you can have it in the back of your mind as an example of the micro-mechanisms that are relevant.
The problem with the monetary model talked about above is that it fits so poorly. It hardly fits the many historical examples we know of the emergence of money, nor does it capture the problems we face today when thinking about money markets (trust in the institutions, the incentive problems inside organisations, the investment problem). Hence it is singularly unsuitable to use as a mental laboratory for the policy problems of today, or even as a descriptive model of the actual roles of money in our economy.

The problem of poor fit carries over to unhelpful advise: despite the fact that it is such a poor fit to reality, it is the only ‘game in town’ when it comes to micro-models of money. A most unfortunate and destructive phenomenon then appears, which is that the only game in town becomes the truth to a whole set of people making their careers on the back of it.

All the potential advantages of models become a disadvantage when a poorly-fitting model is taken too seriously. One potential advantage of models is that they can be the codification of previous knowledge and as such a good model is a quick way of conveying a lot of knowledge to the next generation who don’t have to learn what reasons went into the construction of the model in the first place.

This now becomes a disadvantage: the new generation that looks to write papers ‘on money’ need know nothing about the history of money or its uses today but only need know the dominant model, which turns into a disadvantage because that new generation will come up with twists and extensions of something that is innately unsuitable to answer any interesting question. Yet that new generation will be blissfully ignorant of the uselessness of what they are doing because they, unlike the originators of the first models on money, will lack the historical database in their heads of what actually goes on. They are simply proving their worth by being more acquainted with the mathematical ins and outs of these models than anyone else and that is what supplies them their daily dinner, not whether the model is useful to anyone else.

Another potential advantage of a good model is that you can make consistent statements instead of waffling on incoherently. One real advantage of model-land is that it is fairly easy to spot someone who is not capable of understanding models. This advantage also becomes a disadvantage in a model that fits poorly because you will see a great proliferation of consistent statements that are based on poor abstractions of real phenomena. You might term this the proliferation of ‘precisely wrong’ statements.

And it is a cop-out to say that these precisely wrong statements are not intended to be taken literally: despite being mere models, the adherents deliberately use words that convey its supposed usefulness, such as monetary policy, government, banks, etc. The pretense of usefulness pervades each paper and each grant proposal using these models. Worse still, that modelling community is a group with a big incentive to pretend that the assumptions made for convenience are ‘actually true’, i.e. it is a constituency of individuals with an incentive to presume there is no such thing as transaction costs or a trust problem when it comes to money. When such people become important they will poo poo those who make different assumptions and force them to first invest in their models. In short, a poor model that is taken seriously becomes a part of the problem.

Would you also have the same problems if monetary economics were mainly based on a set of historical case studies and an awareness of the problems faced today by economic actors? Unlikely, because you then at least have set up an ultimate goal of the discipline, which is to understand how the world came to be as it is and to help economic actors shape their world to their advantage, i.e. you are grounding your discipline in historical reality and real world problems. Having said this, one should not be blind to the disadvantage of a more verbal discipline though. The disadvantage is that when knowledge consists of a collection of examples and lessons, there is more room for the wafflers of this world to ply their trade, and there are millions of eager wafflers around.

Are there any good economic models you might ask? I believe there are and my prime example would be Industrial Organisation models of competition and market interaction. These are the Cournot models, Stackleberg models, models of complementary investments in vertical markets, oligopoly models, models of the internet as a platform, etc. The nice thing about these models is that the motivations they presume of their actors (pure greed) are pretty well spot-on and that it is not that hard in reality to see what kind of market interaction is happening, i.e. which of the I/O models to use.

Though it is hard to measure for a statistician, it is not so hard to spot as a human whether, say, the oil companies are engaging in collusion or not. It is not hard to spot a cartel, or the basic information structure of a market, nor is it hard to spot the structure of investment complementarities. In short, I/O models can do a remarkably good job of describing the particular aspects of reality one can optimally intervene in, which is of course why they are so central to the work of regulation authorities and why, for instance, auction design on the internet is done by mathematically schooled geeks. They need to know nothing of the history of auctions to nevertheless be damned good designers of auctions as long as they understand the models and have learned to spot the market patterns around them.

There are thus good models out there and the groups of disconnected geeks working on extending them are, often to their own surprise, doing something useful with their lives. We wouldn’t want to go back to waffling in those areas. The problem is thus not the existence of mathematical models per se, but rather that there are aspects of economic reality where the best we can do is a bad model.

Is money the only area where we can do no better than bad models that are worse than useless when they are taken seriously? Alas, no. What goes for money goes for many economic phenomena. To have an economic model where growth is driven by specialisation (which is what most historical economists believed was the engine of growth) has so far been beyond us, which is why we have ended up with these ridiculous representative agent models. What the pragmatists believe is true about specialisation can’t be modelled by the best minds in math econ land (this is not to say there are no models of specialisation, simply none that get close to illuminating the path-dependence, trust, and institutions that sustain it). Satisfactory ‘des-equilibrium’ models of recessions also simply don’t exist. Models of human behaviour drawing upon more than two of the known ‘irrationalities in our make-up’ are also too hard to solve. The list goes on and on: if one insists on consistent mathematical theorising from ‘micro-foundations’, nearly all of the big drivers of economic growth and economic institutions are beyond our ability to model even remotely realistically.

Mathematical models are hence in many areas a problem because they fit poorly but nevertheless live a life of their own, taking up valuable mental time of smart people, leading individuals to think about the wrong problems, leading people to think in terms of the wrong assumptions, motivating statisticians to measure the wrong things, and divorcing their discipline from reality.

Suppose you believe all this, but nevertheless want to make progress in disciplines by doing proper science, differentiating yourself from the wafflers. What is ‘proper science’ in an area where we cannot make much mathematical headway and hence where we can be reasonably certain that every grand story we tell (in maths or in words) has inconsistent parts to it? That’s the subject of a future blog….