In December, I hosted a session at the Ecological Society of Australia Conference, to be held at the University of Sydney, on how economists may productively collaborate with ecologists and other natural scientists on some of the major issues of the day (be they water, climate, biodiversity, etc.). Some of the notes I used, as well as some examples from colleagues, follow.
The first task I set myself in the brief time I had was to try and express to the ecologists something about “how economists think”. I’m constantly being told jovially by scientist colleagues that “I don’t really know what you economists do”. Which is fine and all, but if we’re going to collaborate productively, we need to have some idea of what we do and how we think. Doing economics in collaboration with scientists is typically more than just “adding some numbers at the end.”
1. How to think about how economists think
First I thought about analogies that non-economists might base their impressions of what economists do on. One analogy is the “machine” analogy—that non-economists think of “an economy” as a machine with moving parts that economists learn to understand and tweak. (This positions economists as engineers, or mechanics of some sort.) An alternative analogy is where the economy is like an “organic machine” (like a human body), with economists being like “doctors” who diagnose and prescribe. These metaphors tend to encourage a view of economists as those who “fix” a machine, whether mechanical or organic, when it breaks down. (I ought to add, I first prepared these notes before the onset of the Global Financial Crisis, in which the idea of economic “break-down” rose high in prominence.)
What I said to the assembled ecologists was that these analogies are not good representations of what economists do and how they think. It’s better to regard economists as looking at a system that has independent self-seeking agents within it, responding to changes in circumstances (including policy settings and actions of other agents). Ecologists should not find this conceptually unfamiliar, given they typically examine systems that respond to outside pressure, with individual agents (species) responding in ways that can be understood using Darwinian principles.
One of the things economists have in their heads is the idea that a given configuration of a system produces a certain amount of “value”. Economists are interested in how different configurations of the system will change the value being generated. Note that while human economic activity generates value (captured in e.g. GDP), value can also be generated by nature in various ways, and that value-generating potential depleted or destroyed by human activity. This is the “space” in which ecologists and economists can fruitfully collaborate.
A potentially important distinction in how economists and ecologists instinctively look at the world involves the relative importance of theory versus data. Economics—even applied economics—tends to start off deductively, referring to theoretical principles before moving to data analysis. Applied scientists often proceed fairly inductively, perhaps with reference to some broad (e.g. evolutionary) first principles and some micro laws. This is fine, in principle, as long as both groups understand this about the other.
2. What economists can (and do) do
One thing economists do is to “measure value”—in simpler terms, “look at the dollars” involved with some proposed aspect of scientific research or policy intervention. Since in the environmental/ecological space, we are talking about being outside the market sector, we are typically talking about “virtual dollars” here. That is, “measuring value” in this context will typically involve what is referred to as non-market valuation.
(Example: I may be able to assess the financial upside to developing a wilderness area and building homes or factories there. Whatever practical forecasting difficulties I face in answering this question, the question itself is simple in principle to answer. The harder question is to assess what the wilderness area is worth being left as it is? This value will normally not be captured in market transactions, but that doesn’t mean the value doesn’t exist.)
Another job economists do is to “measure cost”—this is one part of measuring value in net terms, but in some cases the upside may be too hard to measure. However, economists can look at alternative proposals to achieve a given end and assess comparative costs of those proposals, taking the benefits as given.
Another is “policy assessment” (or “policy design”)—looking at whether a given policy action is likely to have the intended effect. Recalling that in a system comprised of self-seeking agents, they will respond to changes in e.g,. policy settings, potentially in ways not intended by the policy-makers. Hence, policy proposals need to be assessed in light of the Law of Unintended Consequences. Related to this is policy design, in which policy interventions are designed from the ground up to be (for example) “incentive-compatible” and “cost-effective”.
3. Metrics matter
So … we can talk about “systems” that create “value” in different ways, in different configurations, subject to various shocks and pressures. These systems have agents within them who behave in (somewhat) predictable ways in response to those shocks and pressures. (Economists will typically talk about changes in incentives, but that can be regarded for our purposes as simple semantics.)
How members of different disciplines measure things and then predict outcomes can heavily influence our analysis, and hence the recommendations we might make in policy documents and discussions.
Scientists tend to work with physical variables expressed in physical units—metrics—and do their analysis and draw their conclusions based on these. Economists tend to work with a more broadly defined and “dollarized” value kind of metric, even when talking about physical impacts. (I could talk here about economic efficiency, but maybe another time.) Working as economists do tends to lead to different conclusions and prescriptions than if one only worked in physical metrics.
Example 1: Maximum sustainable yield.
If you were asked to give a “sustainable harvest” recommendation over, say, a marine ecosystem (i.e. a fishery), what might you do? A marine scientist (and we’re talking about a hypothetical person here, nobody I know) may compute different sustainable harvests associated with different stock sizes. That is, compute how much a stock of given size grows by in a time period, and let that net growth be the amount that can be harvested. (It keeps the stock constant, hence the harvest is “sustainable”.)
The next step for our hypothetical scientist, after doing this for stocks of various sizes, might be to identify the stock size that results in the maximum sustainable yield—for a standard stock growth function, there will be a stock size that results in maximum stock growth, enabling the largest sustainable harvest. If harvest size is the metric, that’s the right recommendation.
But it’s unlikely to be the recommendation that results in the greatest value from the fishery. The cost of resources devoted to catching fish—“fishing effort”—will vary for different catch sizes, and if you don’t factor that in, you will make a recommendation that fails to maximise the net benefits—value—from the fishery. Focussing only on the physical metric means you maximise the gross value, by maximising the size of the catch.
Example 2: Energy efficiency.
What if a scientist or engineer came up with a way to increase energy efficiency by 5-10%? Is that cause for celebration?
Maybe. You can bet that if it was a University of Sydney researcher, this news would be touted far and wide, and hailed as a great success of the new Institute for Sustainable Solutions by the VC, Provost, and whoever else could make a noise about it.
Meanwhile, the economists would be looking at the finding and saying “Hold on a minute…”
The problem (such as it’s a problem) is that increasing energy efficiency (a physical metric) decreases the cost of using energy (a value metric). This in turn is likely to increase the use of energy. Remember that we’re talking about a system in which agents respond. They’ll respond to things like changes in costs.
Therefore, the impact of increased energy efficiency on actual energy use is an empirical issue. A 5% across the board energy efficiency increase will almost definitely result in a less-than-5% drop in energy use (the gap is called “rebound” in the literature), because of the drop in energy costs. This is likely to occur mostly in the production sector as businesses substitute more (cheaper) energy for other inputs. If the effect is large enough, the net effect is that energy use could in fact increase, a very unexpected physical outcome that could not be predicted by focussing only on physical metrics (this is called “backfire” in the literature, for obvious reasons).
Backfire in energy use is a very strong result to expect from an increase in energy efficiency, and would only result from very particular assumptions about production structures (what economists refer to as input demand elasticities and so on), but the rebound effect is almost certain to exist. Which is to say, a 5% increase in energy efficiency will not translate into a 5% reduction in energy use.
So, while we need to focus on energy efficiency as part of a set of sustainability strategies, we have to remember that making our use of energy more efficient means making energy more attractive to use, which has follow-on impacts.
4. Case studies
Four of us discussed case studies of environmental/ecologic economics research that has used (and could use more) interaction with natural scientists.
I discussed work I’ve been involved with on the economics of ecological resilience—how to think about (and quantify) resilience as a component of overall social wealth, and assess changes over time in resilience and the impacts on net wealth. I also mentioned work done in the area of Market Based Instruments, where market instruments are used to achieve environmental outcomes—in one famous case (Victoria’s BushTender) scientists constructed an “environmental net benefits index”, and economists worked out how to “maximise bang for the buck” from the environmental auctions.
Tiho Ancev spoke on determining economic benefits from environmental water flows based on ecological benefits provided (which appear to be vary substantially across time), and also about research on water quality management (algal blooms) for large dams that cater water to urban water supply.
Greg Hertzler discussed work in the area of bio-economic modelling. He gave an example involving the management of elephants as a potentially endangered species, and made clear how the results were sensitive to assumptions in the modelling process (in particular, looking at raw biomass, versus an age-structured population).
Bob Cairns of McGill University spoke about the economics of the ecological footprint, a measure that has been widely critiqued by economists for being ad hoc and focussed only on physical metrics. Bob is seeking to work from first principles to understand in what setting an ecological footprint measure might yield meaningful information.