Resisting resilience

06/03/2012 § 7 Comments

In graduate school, many of my classes had a focus on ‘sustainability’. We struggled to determine what the word meant in practice, so it became a banner to march behind. A new word has become au courant: resilience. Now, the goal is to promote resilience in our social and economic systems.

For example, there is a new report from the UN ESCAP, entitled Green Growth, Resources and Resilience. The blurb for the report is:

While regional countries are driving the global “green growth” agenda, policymakers are facing a new economic reality and heightened uncertainty. The challenge of eco-efficient economic growth and inclusive resource use is critical and growing in several countries. Fundamental, rather than incremental changes are needed – Governments must therefore take the lead in re-orienting both the “visible” and the “invisible” economic infrastructure. At the same time the implications of heightened uncertainty and risk for policymaking requires more attention.

This blurb manages to mention ‘heightened uncertainty’ twice in four sentences, but never uses the word ‘resilience’. If anything, the report is focused on change: ‘fundamental…changes are needed’ and ‘re-orienting…economic infrastructure’. But resilience is an ability to resist change or to spring back from change. This is the fundamental contradiction with the new jargon. This blurb implicitly acknowledges the contradiction by its inability to speak of both fundamental changes needed and resilience in the same paragraph.

The Econ4 website explains resilience:

A healthy economy is a resilient economy able to withstand unanticipated shocks. To borrow a metaphor from the physical sciences, resilience is the ability to bounce without breaking. We can build resilience into our economy and its infrastructure by following the design principles of diversity and dispersion. The aim is not to maximize “efficiency” at a single point in time, but rather to minimize economic vulnerability over time.

A common analogy for resilience is a bowl. The economy starts off in equilibrium at the bottom of the bowl. Then, a shock displaces the economy, sending it up the side. A resilient economy can return ‘naturally’ to its starting position at the bottom.

This situation is contrasted with an unstable economy. The notion is that an economy at its maximum is on a pinnacle. It can get knocked off its perch and sent rolling down a slope. It won’t ‘naturally’ return to the maximum, but needs to be pushed back (if it ever can).

The central idea is that we should have an economic and social system that is robust to shocks and returns to its initial position. The central problem is that there is no way to determine whether this position is good or not. For example, the failure of Reconstruction and the rise of Jim Crow in the US South after the Civil War shows that the Southern system of apartheid (avant la lettre) was resilient. Here are other examples of resilient socio-economic systems (dates from Wikipedia):

  • Middle Kingdom of Egypt (2055 BC – 1650 BC)
  • Han Dynasty (206 BC – 220 AD)
  • Roman Empire (27 BC – AD 476 (Western Empire))
  • Capetian Dynasty (987 AD – 1328/1792 AD).

Extremely unequal and repressive systems can be stable. In fact, there is a line of argument in poly-sci that democracy is inherently unstable. Dictatorships can be more stable. By the same token, market economies with entrepreneurship can be less stable than command economies with repressed technological change.

To make the resilience concept work, we are back to usual economic questions. Whose welfare? Whose preferences? Who bears the cost of the movement from equilibrium? Who benefits from equilibrium? Who starts with the resources? How are resources redeployed to new uses?

I don’t see how adding this new concept helps us answer any of these questions.

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§ 7 Responses to Resisting resilience

  • Hmm. We didn’t get any of that sustainability stuff at Mason…

  • I think the problem is definitional. Rather than think of resilience as resistance or bouncing back, I’ve found it more useful to think of resilience as managing change – to be able to cope, to adjust, to adapt. In other words, resilience is not the same as long term stability, or returning to the same equilibrium; it’s about being able to find the new equilibrium – our own “sweet spot” – quickly and with a minimum of effort.

    • Bill says:

      When you express it in words, it makes some sense to me. Then I try to think about the model and don’t see what resilience is. Maximising output is clear; minimising variance, reducing the probability of falling below a threshold, maximising a utility function that is non-linear in income — all clear.
      Maybe — just trying this out — your definition of resilience is about minimising adjustment costs over time as the economy moves from one equilibrium to another, with the adjustments created by external shocks. But then we have to deal with probabilistic outcomes and intertemporal concerns, as well as Pareto comparisons between states. Then, I’m back to thinking that resilience isn’t any more useful than sustainability — ‘non-declining capital over time’.
      Do you know of any sources that model resilience?

      • Nobody ever said it was easy! I think there are two things wrong with your conclusion.

        First, compared to sustainability, resilience (as I see it) is very much more dependent on the path taken. Because of that, we have some hope of predicting the future from the past. In that sense, it is a more “scientific” term.
        Second, I see resilience in three dimensions – functionality, resources, and time. if the new normal is less functional than the old, it indicates a lack of resilience. If there is a huge expenditure of resources, or it takes a long time, to get to a new normal then those also indicate a lack of resilience. In principal, one could construct a state function for an entity (e.g., a community), subject it to a disturbance, and then take the integral until a new equilibrium/steady state was reached. One could then compare “volumes” for two (or more) communities and say one was more resilient than the other. In practice, I’m not sure that the view is worth the climb.

        Scott Miles at the University of Western Washington in the US, along with Stephanie Chang in British Columbia. have developed a model of resilience. Very much in line with Adam Rose’s work at Southern Cal. Unfortunately, rather complicated.

        I am taking a slightly different tack in my work – but starting with the classic loss/recovery curve (as Miles and Chang do) to look at community resilience. The basic idea is to deconvolute that into four pieces – the initial (pre-disturbance) psuedo-steady state in terms of functionality, a time-dependent loss function, a time-dependent resource function, and an “efficiency” function that reflects how effectively the community can convert resources (human, physical, or financial capital) into functionality. The generalized functionality of the community is then expressed as F(t)=F(0)-L(t)+R(t)*W(t).

        What I like about this is that it provides four distinct strategies that can be employed to enhance resilience.
        F(0): increase the capacity of the community.
        L: reduce the impacts of disturbances, i.e., limit losses.
        R: have lots of resources available to recover from disturbances – either internal or external.
        W: be efficient in use of resources, e.g., reducing bureaucratic barriers.

        Sorry for length, but I get easily wound up on the subject!

  • Bill says:

    I’ll have to look at the Miles and Chang work, and yours, too.
    I still find these concepts fuzzy. For example, what’s ‘functionality’? It sounds like the sort of word that can mean different things in different circumstances, which makes it hard to provide answers that aren’t pre-determined. Or ‘community’, for that matter. We define communities by saying who is the in-group and who is the out-group. By strategically defining the out-group, we can channel resources any which way.
    Thanks for the detailed reply — it’s given me more stuff to look at.

    • Operationally, we see communities as self-defined. A formal definition we have used is “A group of individuals and organizations bound together by geography and self-interest.” In our work, we are trying to avoid the in-group/have’s-have not’s types of thinking, they are not very useful. That’s probably worth a post and a half on its own.

      I agree that the term functionality is fuzzy. When we talk about functionality, we are talking about the capacity of each of the systems in the community that deliver services. Intellectually, this is OK, but can be hard to measure in practice. It forces us to “parse” the community so that all of the services (economic, social, infrastructural, environmental) are consistently identified. For many of the community services, there are metrics that can be used (However, even here – economy, infrastructure – there is legitimate disagreement about the appropriate metric). For others, there are not (e.g., social), but proxies have to be used.

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