Why Blockchain has no economic future

When Bitcoin went public in 2009 it introduced to the world of finance and economics the technology of blockchain. Even the many who thought Bitcoin would never make it as a major currency were intrigued by the BlockChain technology and a large set of new companies have tried to figure out how to offer new services based on blockchain technology. It is still fair to say that very few economists and social scientists understand blockchain, and governments are even further behind.

I will argue that blockchain has no economic future in the regular economy. I will give you the bottom-line, then describe blockchain, discuss its key supposed advantages, and then take it apart as a viable technology by giving you a much more efficient alternative to the same market demand opportunities.

The bottom line for those not interested in the intricacies of blockchains and public trust

The essence of my argument is that a large country can organise a much more trustworthy information system than a distributed network using blockchain can, and at lower costs, meaning that any large economic role for blockchain is easily displaced by a cheaper and even larger national institution.

So in the 19th century, large private companies circulated their own money, in competition with towns and princedoms. In that competition, national governments won, as they will again now.

The reason that the tech community is investing in blockchain companies is partially because some are in love with the technicalities of blockchain, some hope to attract the same criminal and gullible element that Bitcoin has, some lack awareness of the evolution and reality of political systems, and some see a second-best opportunity not yet taken by others. But even in this brief period of missing-in-action governments, large companies will easily outperform blockchain communities on any mayor market. Except the criminal markets, which is hence the only real future of blockchain communities. Continue reading “Why Blockchain has no economic future”

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.

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!

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 …..

 

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….

What is ‘face’?

I have been part of a research group looking into Chinese migration for about 5 years now (see rumici.anu.edu.au/), and the main cultural difference one has to get used to as a Westerner in interactions with the East is the notion of ‘face’. This Asian cultural trait has been written about for centuries, but I haven’t found a definition that makes sense to an economist used to the language of game theory and utility functions. So let’s look at ‘face’ from an economic perspective, allowing me to make statements on where it comes from and what will happen to it.

To set the scene, consider some examples of the way in which ‘face’ pervades everyday life in China, Japan, and much of South-East Asia. For one, the boss never gets contradicted directly and no-one tells a boss that he is wrong, even if behind his back things are done completely differently and everyone believes him to be wrong. It would thus be quite common for people to congratulate a boss about a decision he did not in fact take. Connected to this, decisions and opinions are obscured in secret codes. By this I mean that it is never said that ‘we dont care about this so we are not going to do it’ but rather the whole topic is avoided or some technical difficulty is fabricated to avoid a negative decision on something. You will thus be hard pressed to hear ‘no, we will not allow you to do X’ but instead will be told ‘we are still working on how to measure X’.

And loss of face is serious business for as soon as you are publicly contradicted and told you are useless, it means that no-one will protect you, help you, or trade with you. Losing face is thus being shut out from a community, which of course explains why keeping up face is a life-and-death thing for many people in Asian societies, even today.

Face thus means people are not directly contradicted; opinions and preferences are hardly ever asked for directly, but instead are inferred; and there is a whole language known to insiders via which to convey actual opinions and coordinate responses.

If you think about this from a game-theoretical perspective you might first naively think that ‘face’ is about people’s beliefs as to how good (or useful or important, etc.) that you are. To have face would then mean people believe you to be virtuous, valuable, important, etc.

This clearly does not fit most examples of face though: it is perfectly possible that someone has face and yet nothing he or she says gets done. What people hence actually think about you does not prevent you from having a ‘face’. As long as efforts are made to hide the truth from you, one still has ‘face’. Hence face is not just about beliefs.

Face is more about the willingness of others to go along with pretending you are good, important, useful, etc. It is only when that pretense becomes unsustainable that one has lost face.

Yet this as a definition is not useful enough because it begs the question why it would matter what others are willing to pretend about you. With well-defined property rights, it matters not what other people think about you since that in no way influences the trades and decisions you can make.

I would therefore venture that the rub behind the whole concept of face is imperfect property rights. With imperfect property rights, it becomes a matter of fluid group opinion as to what you actually own and what you dont. ‘Face’ is then connected to those implicit property rights. The willingness of others to go along with your ‘face’ then signals the degree to which they still respect your property rights and the moment you lose face is the moment all others can rob you of whatever you possess with social impunity.

Translated to a game-theoretical context, this means one should think of ‘face’ as the degree to which others see you as partaking of the social norm upholding a particular allocation of property rights. Their willingness to go along with your face is then nothing less but a social vote as to whether you are still in the club or not. This in turn relies upon a social game in which the accepted rule is that if any two (or more) people deny each other their face then social voting continues until either face is restored or face is lost completely, leading to a re-allocation of the property rights of the loser. Note that what is actually believed about anyone does not matter.

This kind of conception of face has many important implications. For one, it is clear that something like this is more likely to arise in economic systems where most property rights are ill-defined, such as in large bureaucracies where nominally all is owned by the collective (or the emperor who leads the collective) but where limitations of span of control imply that cliques can actually appropriate things for themselves though only to the degree they cover each other’s backs. This of course explains the importance of face for a country like China that has so long had a bureaucracy. It also fits the ‘all who remain in the clique have to stick up for each other’ aspect of face and why someone who has lost face must be killed or in some other way neutralised since there is an outside world who can be alerted to the degree to which these implicit property rights violate the official ones.

Yet, also in more primitive cultures that lack well-established property rights (understood here as allocations that can only be undone by voluntary trades), the same general idea would hold to some extent though one then more normally would call it ‘honour’, and indeed there is an anthropological literature saying that pastoralists (who dont have official lists of who owns what) are big on honour.

The second and perhaps more important implication is that ‘face’ should lose its meaning and value when an economy becomes more monetised and based on formal property rights. Hence the industrial revolution taking place in China right now should be strongly eroding the whole notion of face, at least within the business community. And indeed, if you meet an outspoken Chinese person who says what he wants and tells you what he thinks, it is most likely someone from the business community.

The third, and most worrying implication is that something like face should inevitably start to arise in any major organisation that survives for a long time, since it is in large organisations that property rights become less perfect. Hence the Western world, which has seen greatly expanding government bureaucracies in the last few centuries and where there are relatively large long-lived private enterprises with major span-of-control problems over what all the managers do should see an increase in the importance of face. Whilst ‘face’ thus becomes less important in the East, it is probably on the rise here……