A New Method for the Identification of Patterns in Input–Output Matrices

This paper suggests a new algorithm for selecting the input–output (IO) coefficients of a Leontief matrix in order of importance, so providing an analytical method for decomposing an IO matrix. It avoids the choice of arbitrary thresholds for eliminating flows or coefficients, and allows for circular relationships. For this purpose, a simple inverse-important criterion has been chosen, which is consistent with the logic of the Leontief model. A procedure that greatly reduces the computational burden is then devised. This method permits new comparisons of IO structures of different countries or regions, for identifying their different degree of internal integration and their reciprocal influence through the exchange of intermediate goods. An application to an IO model for seven European Community countries for 1980 is then presented.     

Bargaining power, effective demand and technical progress: a Kaleckian model of growth

Following the Kaleckian tradition, this paper presents a demand-ledgrowth model in which the distribution of income is fully endogenised.This is done by introducing claims on income by workers andfirms. The bargaining power of these two groups affects, throughdistribution, the patterns of accumulation and inflation. Inturn, the bargaining power of workers is affected by the rateof change of employment. The paper discusses the model's static and dynamic implications,including the effects of exogenous and induced technical progress.The model confirms all the typical Kaleckian results, includingthe fact that increases in real wages may lead to acceleratingaccumulation as well as inflation. It also produces a new result:it is possible that an increase in the rate of change of labourproductivity may not lead to an increase in the rate of changeof employment.     

Cassetti's New Method for the Identification of Patterns in Input–Output Matrices: An Alternative Formulation

The least upper bound on the overall proportional error that results from the simplification of an input—output matrix is a useful measure of the information loss. In particular, it is proven that some of the available results on this bound can be used to reduce the computations required for an optimal introduction of zeros. Furthermore, it is shown that the matrix that solves the simplification problem for any given level of error is generally not unique, so that it is possible to impose a priori constraints on the pattern of zeros in the matrix.