Regularities concerning how entry, exit, market structure, and innovation vary from the birth of technologically progressive industries through maturity are summarized. A model emphasizing differences in firm innovative capabilities and the importance of firm size in appropriating the returns from innovation is developed to explain the regularities. The model also explains regularities regarding the relationship within industries between firm size and firm innovative effort, innovative productivity, cost, and profitability. It predicts that over time firms devote more effort to process innovation but the number of firms and the rate and diversity of product innovation eventually wither. Copyright 1996 by American Economic Association.

IT IS WELL KNOWN that least-squares estimates of the coefficients of a regression equation are inconsistent if any of the regressors are measured with error. The nature of these inconsistencies has been examined by Aigner [1], Blomqvist [2], Chow [3], Levi [5], McCallum [6], and Wickens [10] for the case in which a single regressor is subject to measurement error. The purpose of this study is to examine the nature of these inconsistencies when more than one variable is measured with error. We begin by reviewing the case of one variable measured with error, developing a unified treatment of issues which previously have been discussed separately. Concentrating on the case in which two regressors are measured with error, we then examine how the predictions of the one erroneously measured regressor model must be qualified when more than one regressor is subject to measurement error.

Wesley M. Cohen, Steven Klepper, Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D, The Review of Economics and Statistics, Vol. 78, No. 2 (May, 1996), pp. 232-243

Using firm data disaggregated by industry, the authors establish a set of regularities in the distribution of firm R&D intensities within manufacturing industries. The authors show how a simple probabilistic process, in which change influences a key unobserved determinant of R&D and firm size conditions the returns to R&D, can account for these regularities and other features of the distributions. The model provides a unified, noncausal explanation of a series of long-observed relationships across mean R&D intensity, market concentration, and the coefficient of variation. It also offers a novel explanation for the inverse relationship between R&D productivity and firm size. Copyright 1992 by American Economic Association.

The number of producers in the U.S. tire industry grew for 25 years and then declined sharply, and the industry evolved to be an oligopoly. The role of technological change in shaping the industry’s market structure is explored. A model of industry evolution featuring technological change is used to derive predictions that are tested using a novel data set on firm entry, exit, size, location, distribution networks, and technological choices prior to the shakeout of producers. Consistent with the model, earlier‐entering and larger firms survived longer, principally because of the influence of age and size on technological change.

[We consider the nature of the inferences that can be made when all variables in a linear regression are measured with error. Assuming that the measurement errors are orthogonal to each other and the unobserved correctly measured regressors, we demonstrate that the true regression coefficient vector can be restricted to the convex hull of all possible regressions iff all these regressions yield coefficient vectors lying in the same orthant. Otherwise, the set of feasible coefficient vectors is unbounded. For the unbounded case, we demonstrate that prior information concerning the "seriousness" of the measurement errors in the variables can bound the feasible region. Two diagnostics are proposed to indicate the sensitivity of conventional inferences to measurement error in the regressors, and an illustrative example is presented.]