Patents as a Measure of Technology Shocks and the Aggregate U.S. Labor Market

Abstract

I. INTRODUCTION One of the dominant approaches to macroeconomic research in the past several decades has been the island paradigm of Friedman (1968) and Phelps (1970). In this framework, economic agents operate under incomplete information regarding relative and absolute prices when making economic decisions. This paradigm is used in the rational expectations literature (see McCallum 1980 and the references therein) in a market-clearing framework to study, among other things, the effects of fiscal and monetary policies in aggregate economic systems. This market-clearing model is sufficiently popular that it is rare to find a textbook treatment of macroeconomics that omits this classical model of the aggregate economy. One component of this aggregate economic model is a labor market where nominal wages are flexible. (1) This approach to the labor market was developed by Lucas and Rapping (1969); in their model, it is assumed that labor suppliers operate under incomplete information about the aggregate price level, thereby raising the possibility that output produced in the economy will vary with the level of price expectations. (2) Despite its central role in explaining economic fluctuations in a popular model of the economy, there is surprisingly little evidence available in previous research testing the implications of this model of the labor market. One objective of the present study is to provide some empirical evidence on this market-clearing model of the labor market. In carrying out such a test, it will be necessary to provide an explanation for the nonstationary nature of labor market series, such as real wages and employment. One possible explanation for this apparent nonstationarity would be the technology shocks frequently used in the real business cycle literature (e.g., Long and Plosser 1983), where it has often been assumed that these shocks are I(1) unobservable stochastic processes. Thus these shocks to the production function may be used as a way of explaining why macroeconomic time series are also I(1). A similar mechanism involves the possibly nonstochastic scalar preceding the aggregate production function. In the economic growth literature, this magnitude, called the Solow residual, measures the growth in aggregate output that cannot be explained by the growth of quality-adjusted factor inputs in production. This residual is sometimes thought to be measured by the stock of knowledge used by economic agents (Barro and Sala-i-Martin 1995, p. 351). Increases in the stock of knowledge enable the aggregate economy to produce more goods and/or new goods from quality-adjusted resource levels when knowledge increases. If the stock of knowledge is nonstationary, then so, too, would be output and other magnitudes in an aggregate economy. (4) Thus the nonstationary nature of aggregate data can be explained by a nonstationary element of the aggregate production function (whether stochastic or deterministic), however that variable is interpreted. If the sources of the apparent nonstationarity in macroeconomic data are assumed to be unobservable I(1) stochastic processes, an implication of this fact is that the inability to measure technology shocks renders it impossible to test the implications of economic models that would arise if these shocks could be measured. For example, if technology shocks are I(1), then optimizing behavior by households and firms will result in cointegrating relationships between technology shocks and choice variables set by the public. If it were possible to measure shocks to the production function, one could test for these long-run cointegrating relationships and the parameters of these cointegrating vectors could be estimated. (5) In addition, it is desirable to explicitly incorporate technical change into economic models, if it could indeed be measured, because doing so may result in more reliable estimates of structural parameters of economic interest, and it may permit improved tests of economic hypotheses. …