Thank you for the opportunity to participate on this panel.
I thought I might focus my remarks on some of my own policy experiences. Together with some observations on economic theory and current modelling practices – hopefully offering some insights into the “how”.
Let me start with a quote by Keynes that I thought both reassuring and sobering:
“the ideas of economists both when they are right and when they are wrong are more powerful than is commonly understood, indeed the world is ruled by little else”.
I see three key ways economics can help policy advisors:
- How to respond to a crisis.
- Providing ideas to attain objectives – often based on analysis of alternatives.
- Identifying future potential problems.
As economists, we use historical evidence or past events to tease out human behaviours and how they achieve objectives given scarce means and their alternative uses.
The challenge for policy makers is: “how does a discipline that looks to the past – and makes guesses based on potentially biased views of that past – give guidance for the future”.
Principles and theories
First, some comments on theory.
Economics abounds with highly useful principles. Every economist has their own favourites: “constrained optimisation”, “opportunity cost”, “invisible hand”, “comparative advantage”, “revealed preference”, “instruments and objectives”, “information asymmetry” and the “precautionary principle” are some of mine!
Economics is also able to keep reinventing itself. From mercantilism to classical economics to Keynesian economics:
- Rational expectations, adaptive expectations, behavioural economics;
- The Phillips curve, the long run Phillips curve, the expectations augmented Phillips curve;
At times evolutionary – in other cases, we circle back:
- The classical and the new classical and the Keynesians and the new Keynesians. That circling back is generally prompted by a need to explain departures from previously prevailing norms – such as 1970s stagflation or more recently, secular stagnation.
Economics see-saws between periods favouring “the role of government” and those valuing “the efficiency of the market”.
So too with the roles of fiscal and monetary policy in short term demand management.
A stimulatory role for fiscal policy under Keynes fell out of fashion in the 1980s and 1990s when the preeminent role went to monetary policy for short term demand management with fiscal policy to be set in the medium term.
In the post GFC low interest rate environment, we have seen arguments for a more active role for fiscal policy re-emerge.
Little wonder non-economists view us as never agreeing on anything - and that Harry Truman famously asked for a “one armed economist”.
Since the latter half of the last century, the profession has placed growing emphasis on quantitative methods – specifically models and empirical testing.
Over time, models have become increasingly complex — turbocharged by greater data availability and advances in computational power.
They have sought to help advance thinking around key economic principles.
The ultimate dream is that by using copious amounts of data and fine-tuned mathematical models you can solve any and every economic problem, and lock in economic growth and prosperity.
Unfortunately, the real world tells us a different story. The price of theories and models can be a long list of assumptions – often unrealistic.
Nick Gruen warned that “economic theory threatens to become … preoccupied with the work within its models and irrelevant to policy”.1
A unique perspective on where economics helped and hindered
Being an economist, I am going back into my own past to draw insights and answer the question about how economics can contribute to better policy making.
I’ve seen many episodes where economics made a significant contribution to policy development.
It’s a subjective choice, but these stand out for me:
- The reforms of the 1980s. I started my Treasury career in the exchange rate management area in 1983. By the end of the year the Government had dismantled exchange controls, floated the exchange rate, and soon after came financial deregulation. This was complemented by reductions in tariffs that started in the 1970s and micro-reform of the late 1980s. Those reforms drew heavily on many of those economic principles I mentioned earlier. They were also framed by perceived government policy failures in the 60s and 70s — and a shift to reliance on the market for solutions.
- The second episode was the transition from centrally planned to market economies in the early 1990s. As an economist working at the IMF, we were in largely unchartered waters. We drew on prevailing economic theory to design stabilisation programs tackling the role of exchange rate anchors, macro policies as well as the design and sequencing of structural policies; and the speed of the transition.
- I also have the first Intergenerational reports on my list. The first IGR highlighted the potential fiscal gap associated with an ageing population on welfare and health spending. The subsequent IGR featured the population, participation and productivity components of growth and highlighted that the fiscal problem was as much a growth and productivity issue. Together, these IGRs helped create an imperative for policy agendas to enhance productivity, reduce debt, boost participation, superannuation, and lift the age pension age.
- I would add, perhaps controversially for some, the response to the GFC – the “go early, go hard, go households” – built on the experience of the early 1990s recession – I believe did assist in managing the way through the GFC.
- I also want to give honorary mention to the underlying cash balance and medium term fiscal policy of the mid 1990s as well as the introduction of the GST in 2000.
To balance these, I can think of a few episodes where the gap between economic theory and models – and public policy widened. Let me mention three.
The first is the breakdown of monetary targeting in the 1980s. The decade started with consensus between policy makers and academic economists that monetary policy should be based on the achievement of monetary targets. These were predicated on an assumed long term stable relationship between the money stock and nominal income.
I remember arriving in the monetary policy area of Treasury in mid-1984. My section head was a relatively young Matthew Butlin.
By that time, management of the M3 target was already under stress and soon after we were casting about for a new monetary aggregate – either narrow money or broad money – that could display a stable relationship to nominal incomes.
Despite policy makers being loath to part ways, it was a search doomed to failure. The then Canadian central bank governor noted “we didn’t abandon the monetary aggregates, they abandoned us”.
In tackling the “where to now”, as then RBA Governor MacFarlane observed “theory offered little help”.2 Goodhart called it the “increasing divide between state of the art macro theory and practical policy analysis”.3 We subsequently tinkered with a “checklist” approach – itself criticised in a “rules based world” as too discretionary. Eventually, we moved to inflation targeting.
The second example I’ve chosen because I suspect the average person on the street might raise it – the failure to pick the GFC.
In the period preceding the GFC, a kind of script had developed along the following lines: “governments are inefficient, capital markets highly efficient, regulation should be light touch, central banks should take on short term demand management, and business cycles are a historical artefact.”
The GFC of course challenged that script.
In the face of a lot of criticism including from those who seem to confuse economists with astrologers, why didn’t economists pick the GFC?
Well – we now know that Minsky – with his financial instability theory – kind of did.
The problem, or at least one take on it, was that in seeking to paint “a more nuanced picture of the economy and explain specific situations not economics in general, he relinquished some of the potency of elegant models”. Shunning the power of equations and models …contributed to Minsky’s isolation from mainstream theories.”4
The third episode I wanted to focus on is the corporate tax cut proposal of a couple of years ago. Some of you might recall that this was a contested debate - better described as a battle of the numbers.
I believe economic modelling could have played a useful role in explaining the effects of corporate tax changes, and illustrating how these might flow through the economy. Instead, there were competing sets of “facts” and a debate dominated by a constant stream of rival estimates of the effects. Those effects were estimated on very different assumptions, timeframes and using models of varying detail.
My intention is not to go into the rights or wrongs of any of the models. No model is perfect and they certainly do not produce facts. All I wanted to observe is that I don’t believe that the winner was a genuine or helpful debate around the case for corporate tax cuts and their impact on the economy. At the end, I think there was just confusion.
Modelling in Treasury
Krugman once said:
“The economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics for truth”.5
In case this leads to a headline “Treasury abandons models” – unequivocally no!
Treasury has always placed value on models – and indeed – we have a wealth of data and modelling techniques we draw on to inform the economy wide impacts of policies and forecast economic growth, as well as their distributional impacts.
We have an OLGA, EMMA and MARIA. We also have a TIM and CAPITA. These are macromodels, an overlapping generations model, macroeconometric model, an industry model, a dynamic microsimulation model, and model of the personal income tax and transfer system.
More recently, as my colleague Meghan Quinn will outline tomorrow, Treasury is increasingly using firm-level or microdata to complement our aggregate data. This allows us to follow workers and firms over time and offers additional insights into how our economy is operating in areas such as productivity, technology adaptation and wage growth.
There is little doubt that the combination of new data sources as well as computational power offers tremendous and exciting opportunities.
The important point is that as useful as models can be and as rich as data sources can be, their application must always be underpinned by judgement - as they will never be able to capture entirely the complexity of the real world.
The challenge, as Lars Hansen says is “How do we use them in a smart way?”
The answer at least in part lies in having the “appropriate conceptual frameworks”.
We also need to avoid falling into the trap of believing that only that which is accessible to measurement is important. This is sometimes carried to the point where it is demanded that our theories must be formulated in such terms that they refer only to measureable magnitudes.
I also wonder if the availability of modelling has made comprehensive reform that much more complicated. If it has raised the bar by taking insights and converting these into measureable concepts such as ‘winners’ and even ‘relative winners’ – often reported in static terms.
It’s also worth noting that modelling is probably weakest in discussing transitions and dynamics – yet these are some of the most important aspects to good policy design.
To return to my earlier observation - modelling does not produce “facts”. It helps us think through some of the linkages and drivers of potential outcomes. But for this to be useful for policy it needs to be informed by the real world.
This is where consultation and engagement are critically important. Treasury is increasingly recognising that to tackle real world complexity we need to get out there and engage with stakeholders on the detail. Hence, the investment in the Sydney and Melbourne and Perth offices. Also our extensive business liaison program, and the significant amount of consultation we seek to undertake on the development of policy proposals. We are very much aware that we need to understand how businesses and individuals actually make decisions – and the real impacts of policies.
Let me try and bring the above into a few key messages:
- Economics has great principles and a wealth of theories that have enriched the policy makers’ toolkit. The challenge is not being a fashion victim: to challenge the status quo and look for disconfirming evidence. A useful guide might be to keep the classics (and Keynesians) in your wardrobe.
- Unprecedented access to data and powerful models offer amazing opportunities. Avoid falling into the trap of thinking that there is nothing we cannot know with enough data. With apologies to Nick Gruen for borrowing his analogy, avoid the temptation of believing that more data and bigger models will reach heaven in a virtual “Tower of Babel”. Judgement is crucial and the important is not always measureable. And the fact that it is not measureable doesn’t mean it’s unimportant. Engage with individuals, consumers, businesses and other stakeholders is vital as is the individual anecdote.
- The best tool in the policymakers toolkit is humility – as Hayek in the Pretence of Knowledge reminds us, we are dealing with highly complex systems – so no matter what data, theories, insights we have – when it comes to human behaviours, what we don’t know will always exceed what we do.
Keynes summed it up best:
“Economics is a method rather than a doctrine, an apparatus of the mind, a technique of thinking which help its possessor to draw correct conclusions.”6
Cohen-setton, J. 2015 ‘The Triumph of backward-looking economics’
Gruen, N, ‘A lucky boy from a golden age of economics’ The Mandarin
Lowe, P. 2017 ‘Some Evolving Questions’, Reserve Bank of Australia
Macfarlane, I. 1998 ‘Australian Monetary Policy in the last quarter of the 20th Century’, Shann Memorial Lecture, Reserve Bank of Australia
2018 ‘On economists as policy advisors with applications to Switzerland,’ Swiss Journal of Economics and Statistics
‘Is Economics a Science’ The Library of Economics and Liberty
Von Hayek, F. 1974, ‘The Pretence of Knowledge’, The Nobel Prize Lecture
1 Gruen, N. 2018 ‘What’s the beef with Krugman? Gruen on the disciplinary incentives of economics’, The Mandarin
2 Macfarlane, I. 1997, ‘Monetary Policy Regimes: Past and Future’, Reserve Bank of Australia Bulletin, October
3 Goodhart, C. 1989, ‘The Conduct of Monetary Policy’, The Economic Journal, 99 (June 1989)
4 2016, ‘Minsky’s moment’, The Economist
5 Syrios, A. 2017, ‘Economists Are the New Astrologers’, Mises Institute
6 Principles of Economics How Economists Use Theories and Models to Understand Economic Issues OpenStax