Some of the most powerful tools of modern macro lack micro-foundations. Given the sort of micro-foundations that have been attempted to this point in time, I don’t mind that my research employs aggregate demand (AD) as equivalent to the money stock (M) times velocity (V). In other words, the quantity theory as an accounting identity is a useful tool for macroeconomic analysis. For those not familiar, see this simple AD-AS graph.
(Graph from here.)
Output measured in nominal terms is P*y. The quantity equation states that P*y = MV. (This is an accounting identity that provides macroeconomics with its own macro-foundations.) We can replace AD with MV as a result of the accounting identity known as the quantity equation. This presents an intuitive framework for understanding fluctuations in aggregate demand. If M rises (falls), then AD rises (falls). Likewise for velocity which is the inverse of portfolio demand for money – think of portfolio demand (1/V) as cash reserves as opposed to transactions demand (output, y) which is money spent on goods and services.
We know that, for example, in depressions M and V tend to move together. The broader stock of money contracts and velocity falls (demand for money rises). So the quantity theory guides us to consider the composition of and changes in the money stock. We can ask, why the money stock fell during the Great Depression and find that waves of bank failures in the United States led to a monetary contraction. We find that these failures were due to a combination of a fragile unit-banking system which disallowed large banks from operating branch banks and tight monetary policy from the Federal Reserve. The latter was due to a dedication to maintaining high level of gold reserves, which was part of a general increase in demand for gold by central banks. The quantity theory tells us that these factors surely pushed down gold denominated prices and played a significant role in the international crisis.
Research that employs the quantity theory has been fruitful and I assume that it will continue to be so.
While useful, reliance on economic aggregates also obscure analysis. These aggregates do not act on one another directly, but emerge through individual interactions. Causation is not always obvious and requires careful intepretation. Note that I can rewrite the quantity equation four ways:
M = P*y/V
V = P*y/M
P = M*V/y
y = M*V/P
One may dig deeper into causation and define each variable by a set of parameters. Say we define M as a function of output and the interest rate, M(y, i) and y as a function of capital, labor, and technology, y(K, AL). This entails greater specificity and a nice model that we can test econometrically. This may provide us with new information, but we are left operating within a framework that conveys a teleological myth that says, “given starting point A, the economy will move to point B.” Good economists work around this problem, but can we have a branch of macroeconomics that embraces the problem itself?
Richard Wagner thinks so. He observes the problem discussed above,
Equilibrium-centered macro theory can, of course, give an account of interdependence among economic activities. Indeed, such an account is perhaps the prime virtue of this theoretical framework. What it cannot do, however, is give an account of turbulence that arises through inconsistencies among plans because no action is presumed to take place until all plans are mutually consistent. All plans are pre-reconciled within the equilibrium framework, just as the actions of the members of a parade are pre-reconciled. The alternative to the equilibrium framework is to treat the ecology of plans as an emergent process where macro-level objects supervene on micro-level interaction. Any relation among macro-level variables is thus intermediated through interaction among entities at the micro level. (“The Macro Economy as an Emergent Ecology of Plans”, 438)
The economy is itself defined by turbulence and this turbulence cannot be consistently observed via macroeconomic aggregates, though it does generate the macroeconomic data. To posit the macroeconomy as a formula with an optimal solution ignores the competition and conflict that occur at the micro level.
How can one model the sort of competition that I underscored in yesterday’s post? How can one represent an economy that does not move directly from equilibrium to equilibrium? Or an economy without equilibrium in the strict sense? Recent attempts have drawn on string theory where there are different layers of interaction (Potts, 2000; Potts and Morrison, 2007). For their “micro meso macro” framework, Potts and Morrisson explain,
In mmm [micro meso macro], an economic system is conceptualized as being made of generic rules that allow carriers to perform operations (Dopfer and Potts, 2004). A rule and its population of carriers is a meso unit, the macroeconomic system is a complex system of connected meso units, and economic evolution is the process of change in meso units, either through novel generic rules being introduced into the economic system or through a change in the population of each meso rule. Evolutionary macroeconomics is the study of how the entire systems of meso are coordinated and how they change. Evolutionary mesoeconomics is concerned with the structure and population of each generic rule, and evolutionary microeconomics is the study of the individual processes of adoption by a carrier (such as an agent) of the rule. This framework is intended to capture the idea of economic evolution as a process of endogenous transformation of the economic order through the origination, adoption and retention of new economic ideas, or generic rules, that may variously manifest as behaviours, organizing rules or technologies. (Potts and Morrison, 309)
Micro meso macro is a convenient representative case of this new macroeconomics. It emphasizes connections between individuals, resources, firms, etc… and rules about those connections.
The task is far from complete. The challenge is to build and employ models that impart new understanding of market processes and the emergence of institutions. These will be models that include the messy details of individual interaction and of randomness. Within this framework, one can include optimization as one possible outcome without being constrained by its determinism and avoid "throwing the baby out with the bathwater."