THE DETERMINANTS OF THE INTER-INDUSTRY PATTERN OF DIVERSIFICATION BY U.K. MANUFACTURING ENTERPRISES*

Evidence of the diversity of output of larger U.K. manufacturing enterprises’in 1958, 1963 and 1968 is provided in the Reports on the Census of Production. The Censuses show that between 1958 and 1968 diversification was a significant and general trend in manufacturing industries and an important element in the growth of firms during the period. Moreover diversification seems to be part of a longer term trend in U.K. industry and part of the typical development pattern of the large firm. A theory of the firm's diversification decision is proposed and from this theory predictions are made of the structural features both of a firm's primary industry and of outside industries which are likely to encourage diversification from the one industry to the other. The power of the model in explaining the pattern of diversification between SIC manufacturing orders in the period 1963–68 is weak, due in part to the wide variety of factors influencing diversification and to the aggregated form of the data. Nevertheless, the results show the importance of research and development effort in encouraging diversification and the stimulus to diversification given by profitability and risk in firms’primary industries and high rates of output growth in outside industries. While the findings offer no clear conclusions regarding the impact of diversification upon economic performance, the results are consistent with the propositions that (i) diversification encourages technical progress in industry and (ii) diversification increases the efficiency with which resources are allocated between industries.     

Critical factors in technological innovation and their policy implications

This paper explores the critical factors which affect the innovation proccess. Some ways in which some of these factors can be influenced by appropriate policy measures are discussed. In particular, the role of the federal government in promoting innovation is discussed.The role of the government in stimulating and nurturing the innovation process is a controversial one. Proponents of the free enterprise system, many of whom are spokespersons for various industries, have persistently pointed out the deleterious effects of regulation and control on the innovation process. By contrast, advocates of an activist government role are often able to cite the protection of the common welfare as a primary need for government intervention. In truth, the “government” is a patchwork of many governments, operating through many agencies and affecting firms and individuals at several levels and leverage points. The same regulation that has deleterious effects on Industry A may be a stimulus for innovation in Industry B. Even in Industry A, the regulation may act as a force in directing efforts toward certain types of R&D programs that would not otherwise be undertaken.In this paper we examine the specific instances where innovation projects, both product or process types, were affected by government regulation. We also briefly review the critical factors which affect the innovation process and how various governments in industrialized countries have taken actions to influence these factors. Finally, we examine some general guidelines for policymaking purposes.     

When Hard Heads Collide

ABSTRACT . In this article, Loren Lomasky begins with a firsthand account of some of the people he interacted with at the Center for the Study of Public Choice in Blacksburg and some of what he learned there. Aside from the revealing personal anecdotes, we learn something of the culture clash he experienced as a visiting fellow at the Center and about his introduction to intellectual activity as “blood sport.” Lomasky then reviews some of the strengths and weaknesses of public choice theory from a philosophical point of view and offers some suggestions. These primarily concern the theory's narrow focus on the material nature of interests. Finally, Lomasky offers techniques for including moral interests in the deliberations of decision makers.     

The growth of shiftwork in the British food,drink and tobacco industries

The authors develop and estimate an econometric model of shiftworking for male, manual employees in the food, drink and tobacco industries. In the demand equations TSLS techniques are used to take account of simultaneity in the determination of shiftworking, capital intensity and plant size. Market demand variables are also incorporated in the model to allow for the distinction between ex ante and ex post decisions to work shifts. A demand variable is also incorporated in the supply equations. The results confirm the diverse economic characteristics of these industries, suggesting that inter-industry studies may conceal a considerable degree of variation at MLH level.     

Estimation of location of discontinuity in a density

Here we propose a few estimators of θ, in addition to those studied in Goria (1978), the point of discontinuity of the probability density $$f(x,\theta ) = \frac{1}{{2\Gamma (\alpha )}}e^{ - |x - \theta |} |x - \theta |^{\alpha - 1} ,$$ for $$0< \alpha< 1, - \infty< x< \infty , - \infty< \theta< \infty .$$ We establish the consistency and the optimality of the Bayes and the maximum probability estimators. Despite their nice properties, these estimators are not easy to compute in this case and their effective computation depends on the knowledge of the exponent α. Hence, we propose another class of estimators, dependent upon the spacings of the observations, computable without actual knowledge of the value of α as long as it is known that α < α₀ < 1: we show that these estimators converge at the best possible rate. We further demonstrate, using a modified version of the maximum probability estimator's technique, that the tails of the density do not substantially effect their efficiency. Finally a bivariate family of densities, having a ridge dependent on the parameter θ, is considered and it is shown that this family exhibits features similar to the univariate case, and thus, the necessary modifications of the arguments of the univariate case are utilized for the estimation of θ in this bivariate example.