This year’s Nobel Memorial Prize in Economics goes to two Americans, Thomas Sargent (NYU) and Christopher Sims (Princeton). Officially the award is for “their empirical research on cause and effect in the macroeconomy.”
There is no doubt that these two guys are really sharp, and free-market economists can find a lot to like in much of the work of Sargent in particular. Yet to update what I said of last year’s recipients — who studied labor markets — it’s a bit odd for the economics profession right now to be celebrating two scientists for their work in helping policymakers steer the macroeconomy. It would be a bit like awarding Jonas Salk a Nobel Prize in the midst of history’s second-worst polio epidemic.1
The Award
According to the official press release,
How are GDP and inflation affected by a temporary increase in the interest rate or a tax cut? What happens if a central bank makes a permanent change in its inflation target or a government modifies its objective for budgetary balance? This year’s Laureates in economic sciences have developed methods for answering these and many of other questions regarding the causal relationship between economic policy and different macroeconomic variables such as GDP, inflation, employment and investments.
These occurrences are usually two-way relationships — policy affects the economy, but the economy also affects policy. Expectations regarding the future are primary aspects of this interplay. The expectations of the private sector regarding future economic activity and policy influence decisions about wages, saving and investments. Concurrently, economic-policy decisions are influenced by expectations about developments in the private sector. The Laureates’ methods can be applied to identify these causal relationships and explain the role of expectations. This makes it possible to ascertain the effects of unexpected policy measures as well as systematic policy shifts.
For the true econ geeks, I suggest reading the summaries and links provided by Tyler Cowen and Alex Tabarrok (here, here, and here); I do believe their blog was invented to bring everybody up to speed the day of the Economics Nobel announcement each year. For those who want to really dig into “vector autoregressions” (VARs) and the like, start with Cowen’s and Tabarrok’s posts.
For readers of Mises.org, rather than focusing on the econometric modeling techniques, I’ll try to give the “big-picture” summary of the theoretical hurdle that Sargent and Sims tried to overcome.
Theory and History
Mises wrote an entire book on the relationship between economic theory and history; see David Gordon’s lecture here. Although most mainstream economists have never read Mises’s philosophical discussion, in their own way they became aware of the problems during the 20th century.
In the 1950s and 1960s Keynesian economists built models of the macroeconomy that tried to relate variables such as government spending with GDP through historical correlations. In the crudest models, there was no “micro” foundation of an optimizing consumer; the relationships between the aggregate variables were calibrated from past observations.
Although people like Milton Friedman were chipping away at the old-school Keynesian paradigm, the comprehensive assault came in the form of the Lucas critique in 1976. Lucas concluded,
Given that the structure of an econometric model consists of optimal decision rules of economic agents, and that optimal decision rules vary systematically with changes in the structure of series relevant to the decision maker, it follows that any change in policy will systematically alter the structure of econometric models.
For example, historically we might see an apparent trade-off between unemployment and price inflation — the so-called Phillips curve. But if policymakers tried to exploit this historical correlation by running the printing press, this wouldn’t reduce long-term unemployment. Instead, the Phillips curve itself would shift, as workers would learn to expect higher annual price increases and therefore demand bigger wage hikes.
Besides dealing with the thorny problem of expectations and the reaction of intelligent individuals to changing government policies, economists have another huge problem when trying to predict the effects of various measures: the age-old problem of correlation versus causation. Since we can’t run controlled experiments in macroeconomics, it’s notoriously difficult to say conclusively what caused what.
For example, I like to remind people that virtually everyone agrees that full-blown socialism leads to economic disaster. Then I point out that the period in American history when the US government came closest to central planning in order to fight a depression — I’m talking about the New Deal — was hands-down the most sluggish recovery in US history. And in our own times, the economy got worse after the Obama stimulus package than many Keynesian forecasters had predicted would be the case in the absence of any stimulus. What more evidence do we need that big government is bad for the economy?
Yet strictly speaking, we cannot so easily jump from empirical observations to policy conclusions. To switch fields, suppose we wanted to determine if more police would lead to a higher or lower crime rate. It would be incorrect to merely look at reams of data and see if large police forces went hand-in-hand with higher or lower crime rates. This is because government officials might hire more police in those areas that (independently) had higher crime rates because of other causes.
The same is true in economics. Massive budget deficits go hand-in-hand with economies in recession, but the Keynesian economist could say that this is because recessions cause tax receipts to plunge and lead governments to implement stimulus spending.
In the grand scheme, this year’s laureates are being celebrated for their contributions in overcoming these problems of rational, flexible individuals and the confluence of cause and effect in actual data series. The two are associated with the University of Minnesota — a leading “freshwater” macro outpost — and with the role of external “shocks” in the business cycle.
The Proof of the Pudding Is in the Packaging?
As I said in the beginning, there is no doubt that Sargent and Sims are really sharp guys. Given that you wanted to approach macroeconomics in the way the mainstream has done it over the past few decades, then yes Sargent and Sims made seminal contributions and should be congratulated for their important work. For example, there is a poll for visitors at the official site, asking, “Did you know that Sargent and Sims’s work is used by policymakers worldwide?”
Yet hold on a second. We ironically seem to be in the midst of one of the causation-correlation traps that I just explained above. Just about everyone is celebrating the work of Sargent and Sims, in effect saying, “Thank goodness you gave policymakers such guidance, especially when they need it now in the midst of the worst financial crisis since the 1930s! We can only imagine how awful the world economy would be today, were it not for your seminal papers.”
Yet things might well be just the opposite. The “data” is just as consistent with the opposite conclusion, namely that Sargent and Sims steered the macroeconomics profession along a trajectory that led policymakers to do things that blew up the global financial system, such that we are currently worried about the collapse of an entire continent and its currency. What would things have to look like, in order for us to fine all of the most-influential macroeconomists, rather than giving them a $1.5 million award?
Conclusion
Mainstream economics is trapped in its current formalistic paradigm. Although many of its practitioners can recognize that they dropped the ball regarding the current state of the world economy, fans of the Austrian School know that only a thorough study of the insights of Mises, Hayek, and Rothbard will shed light on just what went wrong in the last decade.
- 1Please don’t send me a haughty email: I know Jonas Salk didn’t receive the Nobel Prize in medicine.
