Matt Nolan

“Sluggish” credit growth, where does NZ fit in?

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Via Tyler Cowen on Twitter was this article, combined with the comment “credit growth sluggish”.  The view here is that the 4% growth in private sector credit is too weak when compared with historic averages – and that the goal of the Fed should be to focus on the “quantity of credit” here, rather than target the price.

Now in New Zealand we have similar data here.  According to this, private sector credit growth was 2.3% year-on-year (or 3.2% if we exclude repurchase agreements – which is preferable IMO).  This compares to a decade-long average of 7.3%pa (8.3%pa), and if we were solely quantity focused this would seem insufficient.  [Note:  I did decade instead of history, as the implicit inflation target moved significantly over the decades – a factor that pushes this figure around.  Would be best to look at “real growth” for a longer-term focus]

I’m not concluding anything from this per se – it is just interesting that NZ analysts are running around getting concerned about inflation and rising credit growth, while analysts in the US, who are observing stronger credit growth than we are, are generally complaining that more needs to be done.  Tbf, their unemployment rate is a lot higher, and their output gap is correspondingly larger (presumably).

But with underlying inflation in the lower end of the RBNZ’s target, credit growth numbers objectively soft, and the unemployment rate undeniably elevated I feel that the inflation calls may be a touch overplayed.  [Note:  A relative price lift in construction due to a rebuild in Canterbury is not inflation – it is a signal of scarcity, as prices are supposed to be].

Matt Nolan

The issue of assumptions

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In a recent post, James recently raised an essential, and fascinating, point on assumptions.  To borrow his own words:

Assumptions are often made for tractability, rather than realism, yet still influence our conclusions. It isn’t possible to control for the unrealistic assumptions; if it were we wouldn’t have made them. That means our conclusions will be biased by assumptions we’ve made only for convenience and we need to bear that in mind when considering the policy implications of our models.

This reminded me of an essay by Maki which can be found in “New Directions in Economic Methodology“.  The essay was title “Reorienting the assumptions issue“.

In this essay, Maki does a number of interesting things – but in terms of this specific issue his key insight was to differentiate between “core” and “peripheral” assumptions.  This is an insight we have borrowed many times when talking here on the blog.

A core assumption “constitutes the theory” while a peripheral assumption does not.  What does this mean?  Well it means that the “core” makes up the central set of necessary assumptions required to achieve a certain result!

It is these core assumptions that need to be “realistic” in some sense of the word – while peripheral assumptions are merely there to increase tractability, or make a result easier to interpret.  Fundamentally, our core result should only rely on our core assumptions.

Core assumptions in econometrics

For those who are statistically inclined, this also fits neatly inside our view of econometric theory.  Fundamentally, the assumptions we make about error terms in a regression in econometrics are akin to checks regarding whether we have all the appropriate “core” assumptions for the econometric model and the question we are asking with that model.

For example, we may be trying to explain some independent variable (y) with a set of (presumed to be) exogenous dependent variables (x).  However, it turns out one of these x’s turns out to be correlated the error term – so that this variable is “endogenous“!  If we wanted to estimate the impact of this dependent variable on our independent variable through typical OLS, our estimate will be biased (this is the justification for instrumental variables).  In this case, the model is likely underspecified in some way – such as having missing “core” variables (omitted variable bias), or a missing “causal relationship” (reverse causation between the independent and dependent variables).

A conclusion

As the above example hopefully showed, what dictates a “core” assumption in a model depends strong on what question we are trying to answer with the model!  An analysis of what constitutes “core” assumptions for our analysis, and then a discussion regarding whether these core assumptions correspond to a realistic set of assumptions, should be an essential component of all model building exercises in economics.

jamesz

Behavioural economics and policy

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Stephen Gordon applies the Lucas critique to behavioural economics and nudges:

A key insight of behavioural economics is that people don’t always and everywhere re-optimise whenever their environments change. Instead, they will often – or even usually – make use of various rules of thumb and/or passively accept the default option. …This the idea behind ‘nudges’: you can alter people’s behaviour by making minor changes to the frames in which people operate…

But this only works if the change is subtle enough to not attact the full, direct attention of the decision-maker. If the change is big enough, people will haul out the full artillery of their rational selves in order to try figure out what optimal decision is. This means that behavioural economics is unlikely to be of much use in policy-making.

What he’s saying is a policy that takes advantage of people’s rules of thumb relies on them using the rules of thumb. If the policy is a big enough change in the circumstances they face then they might change their rule of thumb, which changes the effect of the policy. Unless you know how people form their heuristic, you can’t target it with policy.

For example, setting organ donation schemes to be opt-in by default may increase the level of donation because people don’t have a strong preference over whether they donate after death. If you applied the same reasoning to a decision that people care deeply about then it might have no effect because people will make the effort to choose their preferred option.

It’s an interesting point but I think Gordon goes too far by suggesting that it invalidates the targeting of behavioural heuristics. Read more