Mean time of implementation is greater than the mean time of social change

From The Changing Geography Of Education, Income Growth And Poverty In America by Joel Kotkin.

Several interesting points.

In this column, we often rate metropolitan areas for their performance over one year, five or at most 10. But measuring economic and social progress often requires a longer lens, spanning decades.

I think our policy discussions are especially susceptible to this hidden assumption that the human system is usefully stable over long periods of time.

What put me on to this line of thought is the issue with climate change and the underlying models forecasting climate armageddon. Set aside the issue of whether the models are usefully accurate (they appear not to be).

There is a larger issue that is determinative of your assessment of the peril, and that is the temporal framing. Has the climate become warmer (as model forecasts suggested) over the past ten years? No. What about the past 100 years? There is debate but it appears the answer is Yes. What about the past 1,000 years? Pretty certainly, yes.

What about the past 10,000 years? Yes definitely.

100,000 years? No not really.

With the example of climate change, the answer to the question whether the world is warmer now than in the past depends on the time frame chosen.

Which is marginally analogous to the the issue to which Kotkin is alluding.

When we are trying to disentangle multiple interrelated systems (education, investment, growth, regulation,etc.), it becomes very sensitive to time frames and very difficult to link complex interrelated systems over time. We can look at Detroit in 1950 and in 2010 and make all sorts of speculative causal links, but clearly the Detroit of 1950 is not the Detroit of today. We can guess that investments in education might have been a cause of the decline but then we have to account for countervailing issues such as union dynamics over time, the effect of unionization of the public sector, the decline in the competitive political system in the urban context, the increase in globalization, the changing demographic structure (age, race, and class at least), etc. Trying to identify that an investment in education had X outcome becomes almost impossible to determine.

As I observed years ago to a client, the mean time of implementation (of a change) is greater than the mean time of exogenous change. Things are changing faster than you can plan for.

In stable environments you can do a lot of planning and the outcome is substantially determinable by the quality of your planning and the quality of your execution. But in complex dynamic systems that constantly self-adjust and which are sensitive to exogenous changes, the outcome is perhaps incapable of being forecast. The best planning and the best execution will not deliver the expected outcome because the system is evolving faster than your capability to implement.

What can you responsibly do in such an environment? Fall back on shared values and shared heuristics. But what happens when there is not a homogenous culture with shared values and shared heuristics? Ay, there's the rub.

Kotkin's article contains some interesting educational observations. It is a common assumption that higher investments in human capital (via education) are causative of higher future productivity. Kotkin's data indicates that it is more complicated than that and that the evidence is more supportive of a model where growth comes first and thereby makes it more worthwhile to invest in education. The implication is that if you want a more educated population, you should focus on generating high economic growth first and the human capital upgrade will follow. If, instead, you invest in education first and don't attend to growth, you get little return on your investment.