The world of neighborhood demographics can be almost glacial. Long term trends may be understood, but people are generally sticky and slow to respond to change. In the mid-1960’s at the peak of the American automobile industry, the writing was already on the wall for anybody who cared to read it. The automation and globalization of manufacturing was already underway, and the effects on the manufacturing towns of the industrial heartland predictable. But over the course of a decade, these changes progressed slowly and steadily, almost without general notice – until the catastrophic event.
Demographic forecasting, like so many other statistical exercises, relies upon continuity. Linear and non-linear models alike are dependent upon looking at the recent past to forecast the immediate future. And these perform magnificently, most of the time.
Over the past few decades, we have had a number of significant and “unpredictable” events. The 1973 oil crisis. 9-11. Hurricane Katrina. COVID-19. To a certain degree, they were all predictable. But we humans tend to assume that tomorrow will be like today – in California we all know that the San Andreas will at some point trigger a massive earthquake that will have catastrophic effects. But we also know it most likely will not be tomorrow, so we carry on as if the certain event is highly improbable.
Such was the case with COVID-19. Any student of history knows that entire civilizations have risen and fallen due to viruses and bacteria. If you have never read Jerrod Diamond’s Guns, Germs, and Steel (1997), you should do so, it is as gripping as any thriller novel. Back to the point – pandemics are a regular occurrence. But because major pandemics are rare within any human lifetime, we tend to view them as unexpected and are therefore unprepared for them.
Catastrophe, in the mathematical sense, is when the linear continuity of a system is disturbed by an external cause. During the period immediately after the catastrophe, it is very hard to guess where the new “equilibrium” will occur, and our normal linearity and predictability can resume.
It has been a long year, but the effects of COVID-19 on the world are still unknown. We can speculate on questions like “will New Yorkers return after this is over?” and “will restaurant employment rebound?” all we like. The fact of the matter is that it is too early to tell. All of our gauges are still spinning wildly, and some of them don’t actually work (see our discussion of when boring data becomes interesting from a few weeks back).
What we do know is that the demographic shifts — through unemployment, higher death rates, and massive government intervention — have been significant, but may be temporary and will probably take a decade to “work out”. Timely data becomes more important during these periods, but the wise user will always ask the question – are these shifts temporary? Or are they permanent?
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