Economics of spatial-dynamic processes.

Spatial-dynamic processes generate complicated patterns over landscapes. In systems that impact humans and/or are mediated by humans, spatial-dynamic processes generate spatial-dynamic externalities. In complex landscapes the nature of those externalities can also be very complex. There are numerous examples of disease epidemics, for example, whereby disease incidence at any point and time and space is governed by both local factors and also global linkages between and among disease sources (Grenfell, Bjornstad, and Kappey 2001; Keeling et al. 2001). Physical landscapes are rarely homogeneous, and hence the patterns of processes such as disease and pest invasions reflect diffusion rates that differ over space, and often differ with density and the influence of forcing factors. Hence, determining exactly where to initiate controls, how intensively, and when is a difficult problem in a general and realistic setting.

Nevertheless, even our simple modeling suggests some important first principles that inform policy. As we have seen, it is generally optimal to initiate control actions at key places and points in time. Our simple bio-invasion example shows the extent to which early and intensive control near the invasion site is better than later control elsewhere. For many spatial-dynamic processes the potential scale of the biophysical system is much larger than the scales associated with property ownership and control decisions. Under laissez-faire, "keystone" agents will be uncompensated for spillover benefits they confer on others as a result of their own myopic optimization decisions. These keystone agents will thus under-control relative to an efficient systemwide solution.

In a perfect world with no transactions costs, private negotiations as Coase envisioned could eliminate all gains from trade and hence achieve a first-best optimum. But in reality, such costless negotiation is improbable, and we are more likely to witness, if any negotiation at all, only negotiation at the local scale and in a sequentially myopic fashion. It is thus an important empirical question how large the efficiency losses are between the first-best optimum, various second-best negotiated settlements, and complete laissez-faire. The answer clearly depends upon the specifics of each setting. But there is reason to believe that differences are very large in many cases of spatial-dynamic processes that we are currently facing. The reason is simply that spatial-dynamic processes generate network-like connections between many independent decision makers. For processes that expand radially or via transportation networks, the potential size of the network, and hence the ultimate scale of the problem, can be enormous. A recent monograph by McKibbin and Sidorenko (2006) analyzes the potential global cost of flu pandemics, using a reasonably sophisticated model of epidemiology coupled with a global trade model. Depending upon the severity of the impact on laborers, the global costs run from the hundreds of billions of dollars for "small" pandemics to trillions of dollars in damage for large pandemics.

The implications of these kinds of processes, whereby potential damaging impacts spread over time and space in a geometric fashion, are thus potentially enormous. In a fully globalized world where everyone is connected to everyone else, a private decision by one person has potential to have impacts that are multiplied six billion-fold. Recent experiments in economics have suggested that individuals will, under some circumstances, account for public good costs/benefits of individual actions. But these experiments are set in small group, local public good settings, rather than the vastly mismatched scale that is relevant with global spatial-dynamic processes. Large-scale linkages raise critical policy issues about how to manage processes that have the potential to generate global public goods. If we consider the manner in which various countries are considering responses to pandemics, it is clear that they are not too different from our "local and myopic" decision-making procedure discussed above in the bio-invasion example. In particular, to the extent that countries are preparing at all for events like pandemics, they are mostly taking local and contingent protective steps such as stockpiling vaccines that would be given to critical caregivers in the event of an outbreak. In contrast, as we have seen, the most efficient solution is generally to identify and treat keystone individuals or keystone countries early in the process.

The institutional design question is thus: how do we build institutions that can address the prospects of potential large-scale spatial-dynamic public bads in an efficient manner? The efficient institutional solution requires, in principle, institutions that have scale sufficient to encompass the scale of the potential pandemic or invasion. Ideally, the institution must have legitimacy and be able to collect payments, make transfers, take advantage of economies of scale, and centralize decision-making. As the potential scale of the problem grows, however, institutional solutions become problematic for all of the familiar issues associated with collective action. It is not clear whether collective action issues will be overcome for many of the global issues we are currently facing, but it is clear that with globalization, the number of these kinds of problems itself has the potential to grow in an almost geometric fashion.

The author would like to thank Josh Abbott, Julian Alston, Frances Homans, Richard Howitt, Jenny James, Becky Niell, Jim Sanchirico, Marty Smith, and Hiro Uchida for assistance and helpful comments on early presentations of this talk, as well as Paul Preckel and an anonymous editor for helpful editorial comments.

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