If we accept the rapidly growing body of evidence and authority suggesting that many of the core concepts of conventional macroeconomics are bollox, and that economists don’t really know what they’re doing, then the important question becomes ‘What next?’ As conventional macroeconomic theory crumbles in the face of facts, what will replace it?
One of the primary contenders is Modern Monetary Theory, which focuses on money itself (something which, believe it or not, conventional macroeconomic theory doesn’t do). Another possibility is that macroeconomics will learn from complexity and systems theory, and that its models (and, hopefully, their predictive ability) will become more like those used in meteorology and climate science. Anti-economist Steve Keen is working in this direction, influenced by the Financial Instability Hypothesis (FIH) of Hyman Minsky, whatever that is.
But wherever macroeconomics is going, it’s clear that the old order is collapsing. The theoretical orthodoxy that has guided the highest level of economic management for many decades is crumbling. Either economics is an objective science or it’s not. And if economics is not an objective science, then we quickly need an economics that is. Countless livelihoods and lives will be deeply affected by the revolution we are witnessing in theoretical macroeconomics. It may be dry, it may be boring, it may be theoretical, and it may seem incomprehensible.
But it’s hard to think of any discussion that’s more important.
Graham Caswell
The Ongoing Collapse of Economics
4 comments:
"And if economics is not an objective science, then we quickly need an economics that is."
The pot of gold at the end of the rainbow.
I think that what his is saying is what Romer is saying, add empirics to theorizing. The theorists are too much in control of the field though academic and institutional politics.
What Romer is saying is to take a scientific (empirically based) approach comparable to medicine (and the other social sciences) that focuses on results rather than the consistency and elegance of an algorithm regardless of outcomes and even in spite of them.
The present approach is based on the natural sciences in which applied science is based on a theory. That is not the case in the social sciences or psychology (yet). But the social sciences and psychology are different from traditional philosophical approaches in that they are based on inductive studies rather than on intuitively based "principles."
This is an advantage of the digital age and Big Data. Previously, research was limited by availability and quality of data. Now it is possible to shrink the gap between speculation and data.
I get it, more data will help, guide us toward the pot. Managing real, measured problems rather than purely imagined problems would be a giant step forward.
And having theories like MMT with clearly stated goals and well defined mechanisms of action with fewer hidden assumptions is probably an improvement to guide policy and predictions that require planning into the future beyond the wall of uncertainty.
As G. L. S. Shackle emphasized as an economic theorist, THE economic problem is radical uncertainty that cannot be reduced to risk and statistics. Conventional macro assumes a deterministic system that tends toward equilibrium because the real interest rate and general substitution naturally brings economic and financial factors into balance. Given radical uncertainty, that cannot be correct. Moreover, the neoclassical assumptions are so stylized as to be implausible.
Economics is never going to be physics in the sense that there is never going to be an economic correlate of an ephemeris, for example. That's too stylized a modeling approach to be useful other than as a gadget.
The challenge is to decide what can be measured over a time frame and how accurately this can be accomplished. Then look for regularities that hold under specific contexts.
Digital technology enables a lot more data to be gathered and processed quickly. But major obstacles remains in real time in economics,, for example, since a lot of data is proprietary. But governments can and do require timely reporting of data this proprietary with the provision that it will only be used internally until the owners relate it publicly.
A science fiction writer would not have difficulty in creating a command system based on AI that handles everything with the same precision as just on time inventory does for many firms now.
The problem is that economics developed before good data was available and reliance on principles was the principal mode of speculation. Keynes was one of the early birds in working on making better data available through government. This really didn't take off until the 1930's, e.g, with Simon Kuznets developing national income accounts.
While data is fairly abundant and reasonably accurate in the US other developed countries, as well as generally available, eg., through FRED and other data bases, data regarding the global economy is still sparse and unreliable. Therefore, a lot of economics history is just "best guess" estimation.
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