How to tax the platform economy?

In the engine room of nation states, ie the tax departments, the coming battle with platform providers is taking shape. Uber, airbnb, facebook, linkedin, ebay, jobseek, and a myriad of specialised platform providers facilitate micro-trades that are largely untaxed by the authorities. In stead, the platform providers themselves take a cut, partially via advertising and partially via a direct fee for their services. They have taken over an activity that has mainly been provided by governments in the past: places to trade. The town square, the stock exchange, public infrastructure, and the unemployment office are relics of a past where governments were market providers that facilitated trades. Now, it is largely private companies with tax-avoidance structures that have taken on this role on the internet. That role is set to expand hugely.

This is a crucial battle that, so far, the tax authorities are losing because they have not yet grasped the magnitude of the shift. They lack the key new power that they must attain: the power to deny the operation of a platform provider in their country.

At the moment, tax authorities around the world, lead by the Scandinavians whose tax needs are high, are going the usual ‘reporting route’. They are trying to get Uber, Airbnb, and all the other ones to report the trades and the value of the trades that they have facilitated. Understandably, these companies are refusing to play ball because they of course are taxing the same trades themselves in a different way. They are competing with national tax authorities and hence their business model depends on tax evasion, so of course they refuse to help their competitors. Their lawyers make millions from refusing to play ball. The horror example for these companies is the 2015 data on Uber that had to be released to the Dutch tax authorities and that was subsequently shared with Denmark which promptly went after the drivers for added tax payments. This reflected the circumstance that the administration of Uber was in the Netherlands at that time, which allowed the Dutch to force Uber to hand over some of their data, a mistake Uber wont make again. The others too will have learned a salutary lesson from that episode.

Frustrated, the tax authorities are turning to pretty hopeless measures, such as new international treaties on the reporting of micro-trades by private entities. In a race to the bottom between countries trying to attract large companies, that is just a hopeless avenue where the authorities will always be many steps behind the tax-advisers of the big trading platforms.

What are the next moves we might then see when the tax authorities get up to speed? I think two developments are likely: full internet observation by national agencies and government-lead internet firms.

Full internet observation follows the model of China, which now has the capacity to track most of the internet activity of most of the population. That allows it to observe the trades facilitated on internet platforms, which in turn can be used for tax purposes. Those observations can be used to directly go after individual traders or can be used to go after the platform providers, simply by making their activities illegal if the platforms do not assist in tax observations. Adopting the China route would spell the end of internet privacy, but it probably works. And tax is such a key part of the nation state that it in the end trumps privacy concerns.

The second possibility is for the government to re-enter the market for platforms and set up its own internet firms for micro-trades and social media. It can simply copy the best examples on the internet for how to set these things up. The transition will come with losses, but authorities can appeal to national pride to get support from their populations and companies cannot compete with that. For micro-trades within a country or tax region (the US and, in the future, the EU) that should work. For international trades, one should expect more difficulties because government-backed firms from different countries might then directly compete with each other, which in turn might lead to competency battles and new dispute resolution mechanisms.

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!

Repost: The Strange Plan to Securitise HECS Debt

Given that this has reared its ugly head again, here is a repost of a post from April 2013.

The ANU’s Glenn Withers has a plan to securitise HECS debt.

Professor Withers told the HES the main advantage would be that the government “gets the money now”. Rather than waiting for graduates to pay off some $26 billion in HECS debt — with the proceeds conservatively estimated at about $15bn, once forgone interest and non-payments had been taken into account — the government could immediately recoup $15bn in bonds.

The Oz asked both Stephen and myself independently what we thought and, as it turns out, much the same thing.

However, Monash University economist Stephen King said the proposal was just another form of government borrowing.

“All you’re doing is playing around with government cashflows,” he said. “It doesn’t matter where the money comes from when it’s time to pay it off.”

Professor King said the government could just as reasonably issue bonds against expected tax receipts. “You can run the argument for more funding of higher education, but accounting tricks are just accounting tricks,” he said.

Former University of Melbourne economist Joshua Gans said the proposal amounted to “quasi-privatisation of a government asset”, and could have unintended consequences. “It will make it hard for the government to adjust the (HECS) system should it need to in the future,” said Professor Gans, now with the University of Toronto.

“And) it separates ownership from income flows even further. The government has an incentive to ensure that university education works out because that impacts on the flow of HECS repayments.”

This is the full email I sent to the Oz.

Near as I can tell this is just an accounting trick. As the government is in no danger of going bankrupt and there is healthy demand for government bonds, this is a kind-of quasi-privatisation of a government asset, the HECS debt.
There could be real effects from this. First of all, it will make it hard for the government to adjust the system should it need to in the future. Second, it separates ownership from income flows even further. The government has an incentive to ensure that University education works out now because that impacts on the flow of HECS repayments. Take that away and you lose part of that incentive.
If the government really wanted to reform education financing in an economically sensible manner, it would let Universities collect and retain their own fees and hold HECS debt on their books.

The Strange Plan to Securitise HECS Debt

The ANU’s Glenn Withers has a plan to securitise HECS debt.

Professor Withers told the HES the main advantage would be that the government “gets the money now”. Rather than waiting for graduates to pay off some $26 billion in HECS debt — with the proceeds conservatively estimated at about $15bn, once forgone interest and non-payments had been taken into account — the government could immediately recoup $15bn in bonds.

The Oz asked both Stephen and myself independently what we thought and, as it turns out, much the same thing.

However, Monash University economist Stephen King said the proposal was just another form of government borrowing.

“All you’re doing is playing around with government cashflows,” he said. “It doesn’t matter where the money comes from when it’s time to pay it off.”

Professor King said the government could just as reasonably issue bonds against expected tax receipts. “You can run the argument for more funding of higher education, but accounting tricks are just accounting tricks,” he said.

Former University of Melbourne economist Joshua Gans said the proposal amounted to “quasi-privatisation of a government asset”, and could have unintended consequences. “It will make it hard for the government to adjust the (HECS) system should it need to in the future,” said Professor Gans, now with the University of Toronto.

“And) it separates ownership from income flows even further. The government has an incentive to ensure that university education works out because that impacts on the flow of HECS repayments.”

This is the full email I sent to the Oz.

Near as I can tell this is just an accounting trick. As the government is in no danger of going bankrupt and there is healthy demand for government bonds, this is a kind-of quasi-privatisation of a government asset, the HECS debt.
There could be real effects from this. First of all, it will make it hard for the government to adjust the system should it need to in the future. Second, it separates ownership from income flows even further. The government has an incentive to ensure that University education works out now because that impacts on the flow of HECS repayments. Take that away and you lose part of that incentive.
If the government really wanted to reform education financing in an economically sensible manner, it would let Universities collect and retain their own fees and hold HECS debt on their books.

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….

On Macro, the Financial Crisis, Global Warming, and Plato

Jan Libich from Latrobe University is running a televised series on economics. He gets people into his TV studio to talk about some aspect of the economy and then puts it out there. Andrew Leigh, Andrew Hughes Hallett, and Eric Leeper were previous victims. Adrian Pagan and Warwick McKibbin are lined up in future instalments.

It was my turn last week to be grilled on all questions involving macro-economics. It evolved into a lively debate about long-run growth, developments in China, the case for inflation, Plato’s Republic, and the options for averting global warming. A good introductory discussion on all those topics, but in future installments I clearly need to get rid of my smug smile.

If you want to know more about the issues raised, see here.

Impressions of the Occupy Melbourne protest

Occupy Melbourne

Today I happened to walk by the Occupy Melbourne protest and took some photos. They have set up a tent city next to Melbourne Town Hall. It was refreshing to see people with so much enthusiasm. The protestors seem peaceful and reasonably organized. Just as with Occupy Wall Street, several committees have been formed for dealing with various issues (media, parenting, food, legal, etc.). For example they have a communal kitchen which seems well run. We visited the main tent and it was interesting to watch a meeting in progress.

To me, the Occupy Melbourne group seems less `emergent’ than what the Wall Street group has been described as. Occupy Melbourne seems primarily to be an alliance of existing organizations, each with its own agenda that is already formed. These are broad in scope and they go well beyond the financial crisis to also include groups supporting indigenous rights, the Palestinian cause, the Falungong, renewable energy, and several other causes.

The result of this is that while chatting with people from several groups, it seemed they were quite keen to recruit me (and my donations) for their specific cause, rather than being truly in support of an overarching one. It felt a bit like being at a country fair with several different stalls to shop at, rather than a unified political rally. Perhaps this is also the case at the other “Occupy” events? I wonder if these coalitions can really get together because they cover such a broad range of things, some of which are inherently quite distinct. And with so many existing groups, I wonder if new individual voices will actually be heard. Lets watch and see how things develop.

Hot tip: bet one Aussie dollar each way.

At the one extreme we have exotic financial derivatives that no-one knows how to value as well as opaque bundles of high risk loans and low risk bonds that no-one knew how to value either. At the other extreme, we have the simplistic nonsense known as technical analysis that anyone can understand, but happens to be bollocks. No wonder the world financial system is such a ferrel beast.

[DDET Read More]

Age write Lucy Battersby has produce this gem of an article, that spruiks the sage thoughts of Paul Ash, president of the Victorian chapter of the Australian Technical Analysts Association. It is all about the much-buffeted AUD.

It turns out that the Aussie went down for a while, then up, then down, then up a little bit, then down a tiny bit. This is clearly big news. But not one to take things at face value, Ms. Battersby notes that the Aussie is

at a moment of indecision that could see it continue downwards or climb and break though resistance.

Let’s rush straight out and put some money on it to……go up?… go down..? Hell, let’s just buy a Tatts ticket.

But it gets more specific (and consequently more wrong) as the article progresses. Mr. Ash claims that for the next day “it is critical if the AUD can spend 24 hours above 90 cents.” Like Uri Geller and John Edwards though, he never actually completes the prediction of what might happen after that. But he is clearly saying that Tuesday Feb 23 is critical. Forget any notion of EMH or martingales. The claim is that the value Vt of the aussie dollar satisfies the condition

if inf{Vt:t ε Feb 23} > 0.90

then ∂EVt/∂t>0 ….. or perhaps ∂EVt/∂t<0. Take your pick.

Ms. Battersby then chimes in to describe technical analysis as

a search for patterns which not only “provides a theoretical basis” for traders but “removes sentiment and gut feeling” from trading.

The straw man strikes again. The only possible trading strategies apparently are pattern searching or gut feeling. Forget any research on the company you want to own. Someone tell Warren Buffet and his acolytes.

But I have been a little unfair to Mr. Ash in claiming that he never makes a prediction. He does actually come out with one towards the end. He says that if the AUD gets above the “non-confirmed resistance line” of 91.7 cents “then we would say with confidence that the AUD is on an upward trend.”

Anyone heard of a tautology? Since it is below 91.7 now, if it gets to 91.7 it will be on an upward trend. Gentlemen. Place your bets!

Hat tip to Mike Smith for slipping this article under my door.

[/DDET]

Conversation with Ariel Kalil

Ariel Kalil is a Professor of Public Policy at the Harris School, University of Chicago. She is a developmental psychologist by training, and her work links developmental psychology with economics, e.g., the effect of parental job loss on child development. I had a conversation with Ariel about her work and thought it would be of great interest to our readers.

Kwang: Congrats on the NY Times feature last week. Not many social scientists make it to the front page. How do you feel about that?

Ariel: It was very exciting! It was fun having friends e-mail to tell me they’d read the article on their train ride that morning. What I was especially happy about was that the reporter got the story right. He was a very curious and thorough guy, and we spent a lot of time on the phone and exchanging e-mails over the past few weeks. He read all of my original papers carefully, and came up with some very good questions for me. And the families that he interviewed had stories to offer that really illuminated some of the quantitative findings from my work.

Kwang: Tell me a little more about your work. What are main themes you’ve researched and what motivates you to pursue these questions?

Ariel: I’m interested in how socio-economic conditions are associated with families’ well-being and children’s development. So, I’m interested in parents’ mental health and their interactions with one another and their children, and I’m interested in children’s behavior and academic performance. In many instances, there is a link between, say, family income or families’ employment experiences and these outcomes. I care, as all economists do, about whether these links are causal. But, in thinking more like a developmental psychologist, I am also interested in “getting inside the black box” to understand why these links exist, and what kinds of individual differences shape how strong these links are for different types of children and families. I’ve always been interested in applying social science to real world problems. The idea that my work might someday shape public policy that could help improve the lives of families and children is very motivating!

Kwang: In the paper featured last week by the NY Times, you show that young people are badly affected when their parents lose their jobs, and that this is true in single parent and dual parent families [paper]. Could you tell us more about these effects and what you think drives the differences between single mothers and dual-parent families? Between male and female parents becoming unemployed?

Ariel: I think there are likely different factors at play for single parent vs. dual-parent households when jobs are lost. First, these families look a lot different from one another in terms of a whole set of demographic characteristics. So, in some sense, it’s a bit difficult to compare the two kinds of families. One of the biggest and most obvious differences is that when a single mother loses a job, the family has typically lost its only breadwinner. These families are likely to already be strained economically, and to have few (if any) people in their set of friends and relatives who can help them out. In many cases, a job loss sets off a cascade of adverse events that can be hard to stop, such as getting evicted or having to move in with others to save housing expenses, and this might disrupt child care arrangements or where kids go to school, and so on. There is just a lot more instability in these families related to the families’ economic circumstances.

In dual-parent families, I think the situation is a little different, and, at least in the short term, I think the impact on well-being and child outcomes has less to do with the economic impact of the job loss than the psychological one; for instance, in the way that parents relate to one another and to their children. For example, most dual-parent families have two earners, and so the family hasn’t lost all of its income at once. And many of these families also have some resources they can draw on, either savings or help from other family members. The immediate economic threat may not be quite as great. Also, in the families from whom I’ve collected data, I’ve found that parents will typically try to cut back on other things before they cut back on spending for their children, so the kids are often spared disruptions in their daily lives. In these families I think the adverse impacts that we see have a lot more to do with stress and anxiety, which we know can be very damaging to family relationships and ultimately to children’s development. And I think a big factor in the current recession is how long it’s taking people to find new jobs. The number of “long-term unemployed” is at an all-time high, and parents are very worried. We may eventually see more of these families exhausting their savings, losing their homes and encountering the same kinds of hardships that single-parent families have been more likely to face.

The different impact when fathers and mothers lose jobs is a really interesting one. In our work, we have consistently found that the negative impact of fathers’ job losses is greater. And this is not simply because fathers’ earnings losses are greater than mothers’ (in fact, in the US, in 40% of dual-earner households women are the primary earners). This is an interesting puzzle that I’d like to try to figure out; unfortunately the data are not readily available on this particular issue!

First, I think that “stereotypical” gender roles are still alive and well in many families and that the idea of being the “breadwinner” is still very important to many men and that is may be a bigger psychological blow to them when they lose their job. Second, working women occupy a variety of roles – we see in time use data that women still do the lion’s share of caring for children and tasks around the house (cooking, cleaning, etc), even when they are employed full-time. It turns out that working mothers cut back on their sleep and leisure time to do all of these things. So it may be that during periods of unemployment these women spend their time at home more effectively than a similarly unemployed man – because they were already occupying those roles anyway. Also, in the families from whom I’ve collected data, there seems to be more strife over figuring out what fathers’ “roles” are going to be during a period of unemployment. Many fathers viewed spending 40 hours per week in an outplacement office or a networking group searching for a new job as a full-time job, whereas many of their working wives thought they could usefully be spending more of that time helping out around the house or with the children. And that created a lot of conflict, which I think is rooted at least in part in “societal” or individual views about how the responsibilities of running a family should be divided between mothers and fathers.

Continue reading “Conversation with Ariel Kalil”

Which production factor gets destroyed in major recessions, part II?

In a post a few weeks back, I raised the question of what additional production factor one would have to include into the current production function framework in order to have a plausible story about the recent crisis.

That post included a set of conditions any candidate would have to pass in order to fit the current crisis and be interpretable as a true factor of production. From the ensuing reactions, two main candidates emerged: a mystery factor that gives a role to lines of credit (suggested by James A); and input and output linkages (suggested by doctorpat, Ian King, and, implicitly, _Tel).
Let us now add more information to this question and see whether the proposed production factors have something to say about other major economic crises that we have known in relatively recent economic history.

The hope is that we need only one factor to generate a reasonable story for several major downturns. If we’d need a very different new factor to explain each different major economic downturn, then the exercise of looking for new production factors becomes more futile because there is then less hope that having a good explanation for each of the previous downturns will say anything of much use to inform us about what to do to prevent or cope with the next one.

Below is a graph that summarises the GDP movement of three other major economic downturns.

GDP movement during major recessions in the US, Russia and Indonesia

” data-medium-file=”https://coreeconomicsblog.files.wordpress.com/2009/10/gdp3.gif?w=300″ data-large-file=”https://coreeconomicsblog.files.wordpress.com/2009/10/gdp3.gif?w=840″ class=”size-medium wp-image-4524″ src=”https://i0.wp.com/economics.com.au/wp-content/uploads/2009/10/GDP3-300×181.GIF” alt=”GDP movement during major recessions in the US, Russia and Indonesia” width=”300″ height=”181″/>
GDP movement during major recessions in the US, Russia and Indonesia

The blue line shows the Great Depression, in which case the 0 point on the X-axis denotes 1929; the red line shows the collapse of the Russian economy after the changes in 1990; and the green line shows the Indonesian collapse after the Asian Financial Crisis of 1997. In each case, GDP is normalised to be 100 at the start of the crisis and time is re-set to 0 at the start.
The first striking observation is that these three crises are far bigger in magnitude than the current crisis. Indeed, the Russian collapse was so spectacular that I have long wondered how it is possible that our macro-textbooks are not full of insights gained during such a spectacular macro-event. Stiglitz already noted in the 90s that the Russian collapse shouldn’t have occurred under the conditions we still teach as good descriptions of the aggregate economy, but it clearly hasn’t mattered for Western textbooks that a large economy on the periphery did something interesting.
The main question to briefly consider though, is whether the two candidate factors X are known to have been involved in these downturns too? Lines of credit were certainly important in the Russian case (as in the whole of the former USSR), where firms had large amounts of outstanding debt with other firms and the unwinding was a tricky business.

Lines of credit were also important in Indonesia and the Great Depression. Hence credit lines can at least potentially ‘fit’, though it should still be worked out via which actual production factor they affect sold production.

Linkages are clearly of relevance in the Russian case where the whole central coordination mechanism fell away and the ensuing ‘disorganisation’ (A phrase used by Blanchard and Kremer 1997) created many firms who had no suppliers and no clients. Campos and Coricelli in their 2002 Journal of Economic Literature article also point to within-sector reorganisation of links as a probable factor in the collapse.

Whilst linkages are probably relevant in the Asian Financial crisis, it is not well-documented how they might have played a role. We know many city labourers went back to the countryside, however exact numbers are unknown because most people who originally came from the country to find urban employment are unregistered and therefore not included in unemployment and migration data etc (explanation paraphrased from a paper by Tran Tho Dat).
We also know that the capital embedded in collapsing firms was not quickly re-used by others, but there’s no specific account I know of that discusses the collapse in terms of broken linkages.

For the Great Depression, on which acres have been written, I also do not know of anyone looking at it through the lens of links. One might say it is implicitly there when people talk about the issue of bankruptcy, as bankruptcy to a perfect market economist merely means the freeing up of previously inefficiently used production factors. From a link point of view, the importance of bankrupcy is that people and capital are idle for quite a while before they are ‘re-linked’.

Any ideas on how we should think of disruptions in lines of credit and its impact on the real economy via a production factor in these three crises or the current one? Any anecdotes on links?