Resilient Communities are the Foundations of a Resilient America.

Global Hints About Local Resilience

I am an unrepentant data geek.  One facet of my geek-ness is that I am autodidactic (I actually had a Professor call me that – I had to look it up) – I seldom accept others’ conclusions; I have to see for myself (actually, the Professor in question said I had to learn from my own mistakes).  Often that turns into an exercise in looking under dusty rocks, but sometimes I stumble over hidden nuggets.  One of these is the rather portentous sounding FM Global Resilience Index Annual Report 2015.

FM Global has calculated a Resilience Index for each of 130 countries as a measure of supply chain resilience.  However, I’m going to focus on the relationships among the different variables used in calculating the Index because they suggest different ways to look at the resilience of local communities than we normally do.

FM Global bases the Global Resilience Index on data for nine variables it calls “drivers” of resilience.  The data comes from a variety of sources.  Much of the data is derived from surveys; all of it has been put into a common 0-100% scale.

  • Gross Domestic Product per capita (derived from International Monetary Fund data; low are Madagascar and Malawi, high is Luxembourg).
  • Political stability (derived from World Bank World Governance Indicators data; low is Pakistan, high is New Zealand).
  • Vulnerability to an oil shock (derived from US Energy Information Agency data; low is Jamaica(!), high is Chad).
  • Exposure to natural hazards (derived from FM Global proprietary data; low is Costa Rica, high is Ireland). This is related to the fraction of the land area exposed to one or more natural hazards.
  • Natural hazard risk management (derived from FM Global proprietary data; low is Dominican Republic, high is Ireland).
  • Fire risk management (derived from FM Global proprietary data; low is Slovak Republic, high is Costa Rica).
  • Control of corruption  (derived from World Bank World Governance Indicators data; low is Zimbabwe, high is New Zealand).
  • Infrastructure quality (derived from data in the Global Competitiveness Report of the World Economic Forum; low is Lebanon, high is Switzerland).
  • Local supplier quality (derived from data in the Global Competitiveness Report of the World Economic Forum; low is Mauretania, high is Japan).

The first interesting set of relationships is that among infrastructure quality, local supplier quality and control of corruption (click on the graphs below to enlarge them).  Each is correlated with the others reasonably well (R2 values of 0.64-0.73).  It is fairly easy to translate these national results to a local community level.  If corruption is controlled it is likely that infrastructure contractors have used appropriate materials. Reliable infrastructure certainly impacts the reliability of local suppliers.   One can also rationalize a more direct relationship between control of corruption and local supplier quality.  Since infrastructure quality is an important contributor to community resilience, these results imply that the other variables are as well.

 Infra triplet

 The next three graphs are focused on GDP per capita.  GDP per capita is a measure of the economic vitality of a nation; its analog at the community level should also be a measure of economic vitality, hence of the community’s economic resilience.  While there are clearly no linear correlations, we can infer some relationships among the variables.

I’ve added a green diagonal line on each graph to help in interpretation and identified any significant outliers.  If we look at each of these graphs through the lens of fuzzy logic, then infrastructure quality, local supplier quality, control of corruption and political stability are all necessary conditions for GDP per capita (necessary, because for each, their value on the 0-100% scale is less than that of GDP per capita).  In a sense, each of these sets a “ceiling” on economic activity (as measured by GDP per capita).  It makes sense that this would also be true at a local level.

Since infrastructure quality, local supplier quality, and control of corruption are all correlated, it’s not surprising that they all seem to have the same sort of relationship with GDP per capita or that the same country (Luxemburg) shows up as an outlier in two of the three graphs.

GDP triplet

However, the relationships represented by these graphs may be interpreted in another way – there may be a “threshold.”  Lower levels of either local supplier quality, infrastructure quality or control of corruption (up to about 50%) may have little impact on GDP per capita.  This might explain the clustering along the x-axis.  It would then follow that higher levels of these variables then become significant drivers of economic activity.  If there is similar data for communities, it would be interesting to see if similar relationships hold.

There are relationships between GDP per capita and two other variables:  vulnerability to an oil shock and political stability.

Oil and politics

 Once again I’ve plotted them in a way to facilitate a fuzzy logic approach.  All of the comments about the relationships between infrastructure quality, local supplier quality, and control of corruption on the one hand and political stability and oil shock vulnerability on the other also apply.  The graph strongly indicates that political stability and oil shock vulnerability are necessary conditions for GDP per capita, i.e., they set a limit on a nation’s economic vitality.  However, one can also discern a possible threshold effect in both graphs as well.  It should be noted that there is no correlation between any of infrastructure quality, local supplier quality, or control of corruption on the one hand, and political stability or oil shock vulnerability on the other.

The relationship between national political stability and GDP per capita might be especially interesting from a community perspective.  It may imply that community “harmony” (lack of discord) is a necessary condition for a strong economy.  This would imply that a plot of a community’s economic activity vs an indicator of its lack of unity would show a similar relationship.  The oil shock vulnerability vs GDP per capita is perhaps a bit more straightforward. In most countries, their economy depends on oil in many ways, thus their economic vulnerability to disruption of their oil supply.  The anomalously high GDP per capita values of Luxemburg and Singapore may simply be due to their being small countries with lower than normal fuel consumption for transportation.

Finally, a head scratcher.  The plot of natural hazards mitigation vs fire mitigation weakly implied that natural hazards mitigation “implies” fire protection, at the national level, which is reasonable.  However, as the plots show, there is a much stronger relationship between natural hazards mitigation and exposure, but not the one I would have expected.  Rather than exposure being necessary for mitigation, the data suggest the opposite.  I have a feeling that there may be another factor at play (e.g., the perceived severity of the threat).  In any event, it would be interesting to see a similar plot for communities.


As a science, community resilience is in its infancy.  Most sciences start with observations of the real world, proceed to theories to explain the observations, then new sets of observations to test the theories, then revised theories to incorporate the new results and so on.  Looking at resilience data from nations (or states or provinces or regions) can provide us with new perspectives for looking at community resilience.  We need as many perspectives as possible if the infant is to reach maturity.