The Knowledge Production Function and the Malmquist Index Regression Equations as a Dynamic System

When considering the linkage between innovation and productivity, the relationship is often termed as the knowledge production function in the endogenous growth literature. In this article, we approach the issues involved through the aspects of the standard neoclassical production theory. The advantage is that the basic properties required of ordinary production function can be employed to infer the microstructure of the knowledge acquisition process. This approach is also attractive in an empirical sense that the techniques of applied productivity analysis might be effective in investigations on the determinants of productivity and economic growth. As an example, we demonstrate that when the popular DEA-based Malmquist productivity indexes are used in regression analysis the set of linear equations involved can be treated as a system. With reference to the special structure of the knowledge production function, the regression equations can be further specified as a dynamic system. The properties of the Malmquist Index regression equations provide rich microstructures for the relationship between productivity growth, productivity growth components, and their determinants. JEL classification: L10; L96; O30 1 Jinghai Zheng is also Guest Research Fellow at the Centre for China Studies, Tsinghua University, Beijing, China.