Chronocentrism has been defined by British science journalist Tom Standage as “the egotism that one’s own generation is poised on the very cusp of history.” It is to time what ethnocentrism is to ethnicity.
From Fukuyama’s The End of History and the Last Man — “History is directional, and its endpoint is capitalist liberal democracy” — to Rifkin’s The End of Work — “We are entering a new phase in history, one characterized by the steady and inevitable decline of jobs” — to Millenarians, present-biasedness and the belief that the old rules no longer apply seem insuperable for many people.
Nowhere was this clearer than when the world’s population hit seven billion a few weeks ago. Never mind the past 25 million years of human evolution, during which humans always managed to develop technologies to feed themselves. Never mind the fact that famines are man-made and not directly caused by a lack of food to go around. Never mind all that: many commentators saw fit to inform us that the old rules no longer applied, and that we were about to enter an era of starvation and famine.
The Four Most Dangerous Words in the English Language
But chronocentric policy making can be dangerous. The four most dangerous words of investing — “This time it’s different” — are also the four most dangerous words in the English language.
Going back to the example of the world at seven billion, if you believe we have crossed a special population threshold beyond which we will experience constant starvation and famine, you are probably willing to adopt drastic population-control policies that would curtail the freedom to have as many children as they want many people currently enjoy.
Would that be right? And how confident would you have to be that “this time it’s different” to justify a potential loss of welfare spread out over so many people?
There’s No Free (Causality) Lunch
First of all, causality requires identification. Vector autoregressions (VARs) do not provide any automatic or free identification. To do policy analysis with a VAR (as opposed to agnostic forecasting) one has to make the same type of untestable identifying assumptions here as one does in the older, explicitly simultaneous equation, Cowles commission approach.
The most common way of identifying a VAR (ordering the variables and performing a Cholesky decomposition) is EXACTLY the same as using exclusion restrictions to identify a system of equations. Other structural VARS do NOT remove the need for identifying assumptions. VARS are not a free lunch.
That is Kevin Grier, in a post over at Kids Prefer Cheese.
I was looking forward to someone finally saying it, as many of the media discussions of the 2011 Nobel prize for economics somehow made it sound as though the work of Sargent and Sims allowed us to estimate causal relationships.
Not so. The estimation of causal relationships is difficult in the social sciences for the simple reason that we almost never observe the counterfactual. This is especially true in macroeconomics, where the opportunities to run an experiment are few and far between. And even in more micro contexts, we’re not completely sure about much.