Jan Libich recently interviewed Bruce Chapman, who was one of the main architects of the HECS scheme via which university places are financed in Australia, a system that is being copied all around the world now, making Bruce Australia’s most influential international economist by a mile. Bruce talks about this scheme and about the problem of how to manage risk more generally for governments. Follow the link and enjoy!
Vanavil is a school for the poorest of the poor in the middle of Tamil Nadu, India. It started in 2005 as an orphanage/school for the children of two historically nomadic communities left stranded by the devastating tsunami of 2004. Many of the children of these two communities (the Narikuravar and Boom Boom Mattukarar) who were taken in had lost their parents and were destined for a life of begging or worse. Their luck was that a few well-to-do committed people decided to look after them. Now, it is a school of around 140 children drawn from all ethnicities in Tamil Nadu. Located in the countryside, where land and buildings are cheaper and there is less temptation for the children to turn to begging, 10 low-paid teachers are running an orphanage plus elementary school.
One of these committed do-gooders is a friend of mine, Matthew Wennersten, a Jewish American married to an Indian wife who, as a former school teacher, became interested in the fate of these kids. He doesn’t teach at the school nor does he tell them what to do, but he does smooth over things with the state schooling administrators and with corporate sponsors. He wants to increase the shoe-string budget of this school and asked me whether I knew any Australians willing to help out. Thus this bleg. See over the fold for more on this school.
I went to visit this school last week. Continue reading
What’s in a name? In the September 2013 round of re-shuffles, I count no less than 17 changes in names of government departments in Australia, either by some name disappearing or some name changing.
This appears to be a regular game in Canberra. When I worked in Canberra in 2003, there was FaCS (Department of Family and Community Services). Since then, there have been FACSIA (Dept Of Families, Community Services & Indigenous Affairs), FAHCSIA (Department of Families, Housing, Community Services and Indigenous Affairs), and DSS (Department of Social Services). Similarly, we now have DE (Department of Education) whereas we used to have DEEWR (Department of Education, Employment and Workforce Relation), which itself preceded DEST (Department of Education, Science and Training).
Please help me out here, you knowledgeable Troppodillians in Canberra. What is going on with all these name changes? Is someone making money off changes in the stationary?
My confusion partly stems of noticing that some departments change what they do, but keep the same name through the decades. The Treasury comes to mind, which seemingly hasn’t changed its name for 100 years, but has seen major changes in what it does. It has now and then housed bits of the tax office, and currently has responsibilities that didn’t even exist when it was first set up (like retirement income arrangement). Why hasn’t the Treasury changed its name to reflect these changes. We might have then had such exotic specimens as the Department of Taxation, Efficiency, Retirement, Revenue, and Other Regulation. Similarly, the Department of Defence seems to have kept its name ever since 1942, whilst it now has responsibilities it could not have had at the start (missiles, counter-terrorism).
So some kind of game seems to be occurring in Canberra that means some general areas witness continuous upheavals in names, and other areas do not. I truly have no idea what the underlying economics and politics of that game is. Do you know the answer?
A new working paper (to be found here) by two PhD students in our school muses about whether firms optimise profits or returns-to-costs. Normally in economic papers you see the presumption that firms optimise profits, but from the point of view of investors allocating in lots of firms at the same time, it would seem to make more sense to presume they maximise returns. In many situations this will amount to the same thing, but not all situations: you for instance get a divergence as soon as you have some barrier to entry, which is rather common. Its the sort of paper that makes you remember your 3rd year micro lessons. Anyhow, if this is your cup of tea, here is their abstract:
We introduce a theory of return-seeking firms to study the differences between this and standard profit-maximising models. In a competitive market return-maximising firms minimise average total costs leading to output choices independent of price movements. We investigate the poten- tial for mark-ups over cost under both competitive and non-competitive market structures and characterise output and input choices under both, amongst a series of other interesting results. We also extend the model in the case of discrete output and input space and show what conditions are required of demand shifts for firms to modify their production plan.
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 social scientists should do, even if they will often be wrong.
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.
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. Continue reading
Last Monday I posted 4 questions to see who thought like a classic utilitarian and who adhered to a wider notion of ethics, suspecting that in the end we all subscribe to ‘more’ than classical utilitarianism. There are hence no ‘right’ answers, merely classic utilitarian ones and other ones.
The first question was to whom we should allocate a scarce supply of donor organs. Let us first briefly discuss the policy reality and then the classic utilitarian approach.
The policy reality is murky. Australia has guidelines on this that advocate taking various factors into account, including the expected benefit to the organ recipient (relevant to the utilitarian) but also the time spent on the waiting list (not so relevant). Because organs deteriorate quickly once removed, there are furthermore a lot of incidental factors important, such as which potential recipient is answering the phone (relevant to a utilitarian)? In terms of priorities though, the guidelines supposedly take no account of “race, religion, gender, social status, disability or age – unless age is relevant to the organ matching criteria.” To the utilitarian this form of equity is in fact inequity: the utilitarian does not care who receives an extra year of happy life, but by caring about the total number of additional happy years, the utilitarian would use any information that predicts those additional happy years, including race and gender.
In other countries, the practices vary. In some countries the allocation is more or less on the basis of expected benefit and in the other is it all about ‘medical criteria’ which in reality include the possibility that donor organs go to people with a high probability of a successful transplant but a very low number of expected additional years. Some leave the decision entirely up to individual doctors and hospitals, putting huge discretion on the side of an individual doctor, which raises the fear that their allocation is not purely on the grounds of societal gain.
What would the classic utilitarian do? Allocate organs where there is the highest expected number of additional happy lives. This thus involves a judgement on who is going to live long and who is going to live happy. Such things are not knowable with certainty, so a utilitarian would turn to statistical predictors of both, using whatever indicator could be administrated.
As to length of life, we generally know that rich young women have the highest life expectancy. And amongst rich young women in the West, white/Asian rich young women live even longer. According to some studies in the US, the difference with other ethnic groups (Black) can be up to 10 years (see the research links in this wikipedia page on the issue). As to whom is happy, again the general finding is that rich women are amongst the happiest groups. Hence the classic utilitarian would want to allocate the organs to rich white/Asian young women. Continue reading
Economists are wedded to utilitarianism as their collective moral compass. This is why we speak of social planners, welfare, utility maximization, and quality of life. The essence of utilitarianism is that moral judgments are reserved for final outcomes, not the means via which those outcomes are achieved (unless people have preferences over those means). As Bentham said, it is about the greatest happiness of the greatest number of people. In modern jargon, classic utilitarianism is about getting the highest number of total happy life years.
The quiz has 4 questions. My ‘classical utilitarian’ answers and discussions on Friday:
- To which identifiable group should society allocate its scarce supply of life-saving donor organs? I am thinking here of gender, age, race, area, anything that is a potential basis for an administrative allocation.
- There is a potential terrorist of whom there is a probability that he will cause a million deaths and he can only be stopped by being killed. How high should the probability of the threat materializing be for you to agree that your society should have institutions (such as drone programs) that kill him off pre-emptively? And how high should the probability be for you yourself to be willing to kill him off pre-emptively, presuming no other consequences for yourself of that act?
- Suppose you are in the position whereby you alone can choose to make it statistically visible what socially-unwanted things are done to pets by people in their own homes, but no-one knows you have that ability. In this hypothetical, making the data available would in no way change outcomes. Would you make that information visible?
- Suppose you are in the position to decide on whether to have an institution that saves the lives of an identified group of patients, say with a particular genetic or childhood disease. With the same money you could set up an institution that prevents 10% more deaths in the general population, for instance by innoculation or investments in road quality that reduce accident rates. Hence the second institution saves more lives, but the lives saved are not visible, either beforehand or afterwards: even afterwards, you do now know who was saved so the lives saved are ‘statistical’. Would you invest in the first or the second institution? More generally, what is the ratio of ‘statistical lives saved’ to ‘identified lives saved’ you implicitly choose via your policies?
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. In my ‘Fred Gruen’ presentation on this matter in 2004 I used the following graph to illustrate the happiness income relation across countries:
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.
Here is a puzzle for you: what is the theoretical link between bitcoins, Australian coal exports to China, and the US becoming a New Switzerland? It’s a bit of a convoluted link, so see at what stage in the story below you spot the answer.
Bitcoins are all the rage at the moment. With 11 million of them on the internet, each worth a 1000$ today (maybe more tomorrow!), it is a market of 11 billion dollars. This is peanuts in terms of world trade or even Internet trade, which measures volumes in the trillions of dollars rather than paltry billions, but still worth a chat.
Bitcoins have unusual properties as a currency: they are essentially a long string of numbers and characters that uniquely identify a ‘bitcoin wallet’. If you like, your possession of bitcoins stands and falls with a complicated password. Bitcoins can be sent to your wallet by anyone from their own wallets, but only the holder of your password can send from your wallet. So your password, your wallet and your bitcoins are one. For most purposes, there is a finite amount of bitcoins, but at the margin one can create a few more by doing lots of calculations that require a lot of electricity. (I hope you begin to see where coal might come in! )
Now, as a future internet currency, bitcoins are doomed. There are three reasons for that: their limited supply, the ‘greater fool’ principle, and governments.
The finite supply of them (there are 11 million of them now, and there will never be more than 21 million of them as they have been designed to get harder and harder to create) means that, if they are truly used as currency on a large scale in the presence of continuous growth in trade, that their value will continuously grow. This in turn would be its undoing because people would then hold onto them rather than spend them, anticipating that future value increase, meaning they are no longer used as means of exchange and their market collapses. It is a classic hoarding collapse of currencies seen before in money history.
The ‘greater fool principle’ will also kill off bitcoins: Continue reading