Self-Organization and Design in Capitalist Economies

摘要

Fluctuations in aggregate variables like output, prices, and employment pervade economic dynamics at all time scales: from minute-to-minute change in a stock price, monthly swings in industrial production, and annual variations in GDP to the longer phases of structural change like those identified by Angus Maddison (1991). Fluctuations in the same variables happen also at spatial scale: spreading from single industry sectors to national economies, to regions, and up to the world economy. To explain why economic activities produce instabilities and to find consistent patterns in a flux of change has always been at the core of macroeconomic research and theorizing. In a previous work (Matutinovic 2005) I dealt tentatively with the first question in the context of microeconomic origins of business cycles. Here, I will address the second one, namely, one statistical pattern—the power law—that emerges in the U.S. business cycle and in other economic phenomena and which is probably pertinent to all market economies. Power-law distributions, also known as Pareto and Zipf distributions (Adamic 2003), show the inverse relationship between the size of an observable and its frequency. As such, they have been known for quite a long time in economics: distribution of income and wealth (Pareto law), size distribution of firms (Simon and Bonini 1958), and size distribution of cities (Zipf's law) are among the most famous instances. Referring to the latter phenomenon, Paul Krugman remarked that "rank-size rule is a major embarrassment for economic theory: one of the strongest statistical relationships we know, lacking any clear basis in theory" (1997, 44).