Markets are a powerful policy tool. The market mechanism allocates scarce
resources to their highest value users, so enabling a policy objective such as
a cap on overall water use or carbon emissions to be met at the lowest possible
cost to society. By their very nature, markets will follow their own path. A
well functioning market will find the genuinely efficient price for a scarce
good – and will allocate resources optimally in the process. The attraction to
the policy maker is that there is less need to know the internal (and hence
basically unknowable) circumstances of the regulated parties in order to create
efficient policy. However, this also means that market outcomes (prices
achieved, innovations brought forward) cannot be predicted with certainty, and
are likely to differ from the outcomes that would be reached by non-market
regulatory policies.

Image:
Thomas Hawk

There is currently great interest in
the process of designing and implementing markets for public policy due to the
forthcoming Australian Emissions Trading Scheme (AETS) for greenhouse gas
emissions. There is much debate and lobbying around designing a set of market rules
which will work ‘best’. As the latest discussion paper from the Garnaut
Review points out, these rules should be simple and consistent. A growing body of evidence from existing
environmental markets, as well as laboratory market simulations, shows that the
details of market design can have a very significant impact on market outcomes.
Not all markets work equally well. The
more complex the rules, the harder it is to predict exactly how a market will
work, and the greater the chances of it failing in its task of efficiently
allocating scarce resources. Therefore for any market-based policy, and
particularly one of such scope and impact as the AETS, great attention must be
paid to the details.

Economic theory provides a starting
point for market design. However people frequently do not conform to the narrow
assumptions of economic models, which can lead to unanticipated consequences.
Sound market design therefore requires real human behaviour to be incorporated
into economics. One approach to doing this is experimental economics (pioneered
by Nobel laureate Vernon Smith), which observes economic
decision-making under controlled laboratory conditions. In a typical economic experiment, participants
trade in a simulated market through computer terminals. To increase the realism there is real money at
stake – participants are paid based on their success in the market.

While experiments are by no means the
final word on economic behaviour, they provide many insights which can guide
policy design. The first and most crucial is confirmation that, when exposed to
the vagaries of human behaviour, the details of market design really matter.
For example, the ‘irrational exuberance’ which leads to asset price bubbles and
subsequent crashes is readily observed even in simple laboratory markets in
which people are trading artificial assets with clearly defined values. Modifying the rules of the market to improve
availability of information about supply and demand, for instance by
incorporating a simple forward (futures) market, can dramatically reduce this
volatility (1). In a forward market
participants can agree on prices for future trades, which effectively provides
advance warning of future market conditions.

In terms of an AETS, the Garnaut Review recognises the importance of
forward markets in providing sound price signals and hence guiding medium and
long term investments in alternative technologies. To some extent forward
markets emerge spontaneously – in fact, some market participants (particularly
companies involved in electricity generation and retailing) are already
trading forward against the anticipated compliance units, even though they have
yet to be defined (or even named). For a forward market to function well,
information about future permit availability must be made available as openly
and accurately as possible. Dysfunctional forward markets will distort spot
(current) markets and lead to poor investment decisions.

A similar issue arises with the
creation of offsets and the banking and borrowing of permits proposed by
Garnaut. The number of permits in the
market, needs to be publicly known to guide
future investment. Experimental
simulations of the Mandatory Renewable Energy Target (MRET) market suggest that
a lack of transparency in the number of permits created is likely to have
impaired market performance (2). When market participants were able to secretly
hoard permits, many did so, resulting in low supply and rising prices early on,
followed by a price collapse as it became evident there was an oversupply. Similar
patterns have been seen in the EU ETS and the NSW Greenhouse
Gas Abatement Scheme (GGAS) markets, in part due to uncertainties
around the number of permits actually in the market.

Another key market design question is
the initial allocation of permits. Garnaut indicates that there is no case for
freely allocating permits, so they should therefore be auctioned (with
compensation for trade-exposed industries and adversely affected regions and
households). In addition to its public finance merits, this proposal also works
from a behavioural economics perspective. Experiments show that people tend to
place a higher value on items they hold than on those they do not (3) – in
behavioural economics, it appears a bird in the hand really is worth two in the
bush. As a result people prove poor at trading items which they have been given
for free. Running an initial auction forces all market participants to consider
their optimal position and place a reasoned value on permits, and is likely to
lead to more ‘rational’ and efficient subsequent trading activity.

Psychologically people relate very differently
to losses and gains (4). The prospect of incurring a loss has a far greater
mental impact than the prospect of an equivalent profit. Whether an outcome is
considered as a loss or a gain depends on the way the decision is framed. In
laboratory experiments on simulated emissions trading schemes, people will go
to far greater lengths to avoid paying a penalty for being non-compliant than
to make a profit from being compliant, even though the financial consequences
are identical. Paying a ‘penalty’ is not only seen as a loss, it also implies
bad behaviour, which has reputational consequences. This may explain the
apparently irrational behaviour seen in the NSW GGAS market in which some
market participants were actually prepared to pay slightly above the penalty
rate in order to avoid being non-compliant (which also created a significant
problem in the forward market – see 5). Therefore the way in which any
sanctioning mechanism is labelled (e.g. penalty, levy, make-good provision) is
likely to impact on market behaviour.

Great care should be taken around the
design of any initial permit auction within an AETS. There are parallels
here with the auctions run by governments around the world for 3G mobile phone
licences in 2000-2001. Each government opted for a slightly different auction
design, which contributed to the vast differences in revenue raised. Once again
the details really matter, and the dividends from thorough design and testing
can be huge. The economics laboratory provides a ‘policy wind tunnel’ in which
proposed market mechanisms can be tested and refined, reducing the risk of
costly policy failures. There will certainly be surprises in store in terms of
how people and firms, individually and collectively respond to the new market
signals arising from an AETS.

A
final thought concerns the future of voluntary abatement activities and carbon
markets as we move into a statutory AETS. Simple economic theory would suggest
that introducing additional incentives through an AETS can only increase
people’s abatement activities. However quite the opposite may be the case, and
there is evidence to suggest that introducing regulations or financial
incentives can lead people to stop doing things voluntarily (6). If voluntary
carbon markets are to have a future they will need to be very clearly
delineated from an AETS, perhaps by focussing on sectors or activities which
fall beyond the scope of the statutory market. While many people donate to
medical charities, no one donates to Medicare. Why should carbon be any
different?

1)
See Porter, D &
Smith, VL (1995) Futures contracting and dividend uncertainty in experimental
asset markets, Journal of Business 68, 509-541 and Kluger, BD &
Wyatt, SB (1995) Options and efficiency: Some experimental evidence, Review
of Quantitative Finance and Accounting 5, 179-201.

2)
Nolles, K (2006) The
impact of the delay between energy generation and REC creation in MRET: A
laboratory investigation on the performance of the Australian Renewable Energy
Market. Aton
Experimental Economics Laboratory.

3)
Kahneman, D, Knetsch,
JL & Thaler, RH (1990) Experimental tests of the endowment effect and the
Coase theorem. Journal of Political Economy 98, 1325-1348

4)
Kahneman, D & Tversky,
A (1979) Prospect theory: An analysis of decision under risk. Econometrica
47, 263-291

5)
Nolles, K (2006) How
the forward market has raised the penalty rate for MRET, GGAS and GES by 20%. Aton
Experimental Economics Laboratory.

6)
Frey, BS (1997) Not
Just For the Money: An Economic Theory of Personal Motivation and Reeson,
AF & Tisdell, J (2006) When good incentives go bad: An experimental
study of institutions, motivations and crowding out.