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To many economists there is a clear interrelationship between microeconomics and macroeconomics. If asked by a student to explain how micro profit maximization constrains a macromodel, the economist might describe aggregate supply as an aggregate production function, and the demand for labor as a derived demand which, under competitive profit maximization conditions, can be set equal to the real wage rate. If asked about the relation between the theory of consumer behaviour and the theory of the consumption function, the economist may produce a sophisticated utility maximization framework. Constraints involve income and wealth such that, for given tastes and prices, real consumption demand depends on real disposable income appropriately defined.
The microeconomic foundations of the aggregate demand for money can be similarly located in choice-theoretic portfolio analysis or inventory analysis. The demand for capital goods may be studied as part of firms' desires to maximize discounted net worth.
If the student wished to press further, there is a set of questions which could be developed at a higher level of abstraction. “How,” it might be asked, “is it possible to generate labor market excess supply in equilibrium, when in microeconomics we were taught that excess demand was zero in equilibrium?” The standard answers here probably vary with the training of the instructor, but most economists would describe the difference between partial equilibrium analysis and general equilibrium theory, and suggest that in a multi-market framework, disturbances or structural rigidities in one market may induce disequilibrium in another market.
“The price we pay for anticipation of the future is anxiety about it. Foretelling disaster is probably not much fun; Polyanna was much happier than Cassandra. But the Cassandric components of our nature are necessary for survival. The doctrines for regulating the future that they produced are the origins of ethics, magic, science, and legal codes. The benefit of foreseeing catastrophe is the ability to take steps to avoid it, sacrificing short-term for long-term benefits”
[Sagan, 1977, p. 71].
Current reinterpretations of the microfoundations of macroeconomics require examination of the structure of the macroeconomic theory that has emerged in the forty-plus years since Keynes “revolutionized” the discipline.
Unfortunately, there is today no accepted view of what it was that Keynes actually accomplished. There is no easy answer to the question of how standard macroeconomic analysis differs from Keynes' own vision. It would seem that we must tackle the problem which has occupied theorists for decades, of identifying the system of Keynes and tracing its transmogrification at the hands of both Keynesians and anti-Keynesians of innumerable persuasions. This herculean task might not, however, untangle current analytic problems.
For better or worse, questions about “what Keynes really meant” stand apart from the creation of rigorous, analytically tractable, and sufficiently rich microfoundations structures. We are interested in studying those “generalized” general equilibrium structures which generate macroeconomic insights. Consequently this chapter will set out various elements of the General Theory in a form which will motivate our discussion in Chapter 4 of the early microfoundations of macroeconomics literature of Hicks, Lange, Klein, and Patinkin.
What constitutes an “appropriate” microfoundation for macroeconomics has been answered, implicitly, by most economists brought up in the neo-Walrasian tradition. If any single point of view can be said to have prevailed, it is that the micro–macro bifurcation is rectifiable since a well-specified general equilibrium model describes the behavior of all economic agents in an economy and a fortiori describes the behavior of those agents when they are considered generically, as representative sectoral agents (consumers, capital goods producers, etc.).
Despite this consensus most economists, when distinguishing between microeconomics and macroeconomics, will talk about the level of aggregation at which the various models work. As H. A. John Green [1977] has written: “It is clear that macroeconomics, by its very nature, involves aggregation” [in Harcourt, 1977, p. 179]. In microeconomics the emphasis is on individual behavior, actions of households and firms, and choice calculus. In macroeconomics, those same units are treated as aggregates: all households generate a demand for consumer goods, for instance. Consequently the translation from micro to macro necessitates aggregation rules which enable the choice-theoretic household demands for consumer goods to be combined into an aggregate consumption function. Just as at the individual level prices, tastes, and income are needed to generate demand curves, so too at the macro level some “appropriate” real concept explains real consumption demand. Consistency requires that individuals are sufficiently similar so that their behaviors may be summed.
This paper is primarily devoted to a study of the (static) optimality properties (e.g., Pareto-optimality of the equilibria) of certain resource allocation mechanisms. It is shown that one such mechanism (the “greed process”) is optimal in a class of economic environments much broader than the class for which perfect competition is optimal. More specifically, the greed process has the desired optimality properties for all environments from which so-called external economies or diseconomies are absent; unlike perfect competition, the greed process does not presuppose the absence of indivisible goods, of discontinuities, or of increasing returns. However, the greed process lacks the dynamic (stability) properties known to hold for perfect competition, at least in certain special cases.
That the greed process does have certain optimality properties would be of little interest, were it not for the fact that it belongs to a class of informationally decentralized processes and hence shares with perfect competition a feature that has been extolled as one of the main virtues of the classical market mechanism. Still, just because it is designed to cover a broader class of environments, the greed process calls for more information (is informationally less efficient) than the competitive mechanism. To illustrate this, a variant of the latter (called “quasi-competitive”) is constructed and is shown to have the desired optimality properties when the environment satisfies the usual divisibility and convexity assumptions, while requiring less information than does the greed process.
In this paper, we wish to discuss the bearing of some recent developments in mathematical economics on the problem of the optimal allocation of resources. We will confine attention here to an economy whose aims are well defined. That is, we assume that the preferences of the economic system can be embodied in a utility function which depends upon the outputs of commodities. For a given technology, the possibilities of different output combinations are restricted by the availabilities of primary resources. The problem of optimal resource allocation is to choose among all the feasible combinations of production processes that combination which maximizes the utility achieved by the economy.
Since the discussion is at a fairly high level of abstraction, the economy being studied may be a nation or some smaller economic system, including a single firm. The assumption that a single utility function represents the objectives of the economy fits best the case of a firm. For a nation, the assumption is less justified, but it provides an introduction, at least, to the more complex problem raised by the presence of many individuals, each of whom judges the workings of the economic system in light of his own utility function. We also avoid the subtle problems involved in defining optimality in the more general case.
The problem of choosing the allocation of primary resources among different productive processes so as to maximize a prescribed utility function is a mathematical one, and its solution in any concrete case can be regarded as a matter of computation.