An Incentive Compatible Model for Higher Education deregulation

Given the level of opportunism occurring in the Australian Vocational Education & Training higher education sector since deregulation (uncapping places & fees), recent articles:

I think it is worth reblogging my Senate submission (Feb 2015) suggesting a new incentive compatible model for a deregulated higher education market where education providers have ‘skin in the game’.  This Senate submission provided a solution to what I saw as a fundamental misunderstanding of the risks associated with deregulating higher education within the current policy framework, published as an opinion piece in The Australian (Oct 2014):

This was followed up by an article calling for universities to have more ‘skin in the game’ (Mar 2015):

I presented this model at the ANU Forum on Higher Education Financing, Friday 13th August 2015, on the topic ‘Should universities have skin in the game?’.

This model can be applied to any type of higher education provider where students have access to government administered income-contingent loans. Whether providers be universities, vocational, professional bodies or dedicated postgraduate institutions.  This model can even be applied to specific types of courses which are regulated separately, such as proposed Australian university flagship courses.

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The drunk neo-classical economist and the lamp post

Alan Greenspan’s recent article ‘Never saw it coming’ reminded me of the ‘drunk and the lamp post’ joke:

A drunk loses his keys and is looking for them under a lamp post. A policeman comes over and asks what he’s doing. “I’m looking for my keys” he says. “Where did you lose them?” the policeman asks.  “I lost them over there”. The policeman looks puzzled. “Then why aren’t you looking for them over here?” “Because the light is so much better here”.

For neo-classical economists these ‘lamp posts’ are mathematically elegant and tractable models, sometimes supported with econometrics, which lead to unambiguous conclusions. While these models are illuminating and logically consistent within themselves they are frequently ‘mugged by reality’, as Greenspan puts it, making these models a poor basis for forecasting.

This is an ‘observational bias’ where people focus their attention on areas leading to easily illuminated results.

An example in banking & risk management is the over reliance on risk models such as Value-At-Risk (VAR). Which, while mathematically complex, conveniently produce an ‘output’ which is easy to interpret and explain. This leads to an overconfidence in mathematical models measuring a bank’s risk exposure while the more dangerous risks, in hindsight after the GFC, are the organisational behavioural risks which tend to be largely ignored (in the shadows).  Including ‘model risk’ arising from cognitive biases affecting how models are constructed and interpreted rather than mathematical error per se.  Well known mega-losses where sophisticated risk models were mugged by behavioural reality are: Societe Generale’s rogue trader (USD$7billion), Morgan Stanley’s ‘hedging error’ (USD$8.7billion) and the bankruptcy of Lehman Brothers.  I worked for 2 of these 3 banks and turned down an offer from the third.

There is also an ‘observer bias’ where how we see things is not independent of our own condition, preferences and context.  Neo-classical economists tend to view all individuals in their models as being as rational and mathematical able as themselves even though in reality individuals tend to have a wide range of cognitive ability.

“We don’t see things as they are, we see them as we are.” Anaïs Nin

A rather brutal joke I often heard in banking, mostly from traders, was:

Q. What do you call an economist making a forecast?

A. Wrong.

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