PRODUCTION FUNCTIONS AND THE DISAGGREGATION OF LABOR INPUTS IN MANUFACTURING PLANTS*

ABSTRACT. This paper investigates the assumption of homogeneous labor inputs underlying the production functions in many regional models. The results indicate that nonproduction and production labor within manufacturing plants are not strongly separable, and the homogeneous labor assumption is not therefore appropriate. Hence, regional models need to take this into account in order to give reliable results. Differences between industries provide useful additional insights into their labor and capital relationships.     

Racial Stratification, Intellectual History, and Presidential Action in the Civil Rights Domain

Using an historical approach, this paper assesses the role of the American presidency in the pursuit of civil rights policy. The basic argument is that the drivingforces in the passage of civil rights policies since Reconstruction have been external pressures on the presidency. Rather than being protagonists in the progression of civil rights, presidents are portrayed as political actors primarily interested in maintaining social order and attracting African-American votes. Rarely have presidents pushed for civil rights progress outside of that context.     

A Model to Relate the Size of the Central Business District to the Population of a City

A set of dynamic models of CBD growth are developed. They hypothesize that growth rates depend upon the population growth rate together with the increasing unwillingness of people to travel to the city center as city size increases. A prediction of the model is that absolute decreases in CBD size can occur associated simply with population growth rather than technological change as is usually postulated. The models are tested using retail sales data for the United States. Good predictions are generally found, especially when the unique CBD of New York City is removed from the data set. However, the model which has the greatest theoretical validity relative to central place theory does not have the best fit to the data. Implications of this are discussed, along with those of utilizing transformations in regression models.     

EMPIRICAL IMPLICATIONS OF ADVANCES IN CANONICAL THEORY*

Canonical correlation has seen growing acceptance in geographical research as a tool for analysing the interrelationships between two sets of variables.¹ It provides a natural extension to the multivariate case of simple correlation analysis introduced into the discipline in the fifties for measuring the degree of areal association between two individual variables.² It has also proved valuable for forging a link between traditional geographic variables measuring the attributes of places and those indicating interactions among them.³ Recently, major developments in canonical theory have occurred which provide two major benefits for geographical research.⁴ First, asymmetrical regression relationships in addition to symmetrical correlation relationships between two variable sets can be determined. Researchers can use canonical regression to examine the degree to which one variable set is capable of predicting the other, in addition to canonical correlation which examines the symmetrical interrelationships between the two.⁵ Secondly, much improved methods are available for measuring the number, strength, and nature of the interrelationships between the two variable sets, and for assessing the adequacy of the canonical model in general.⁸ The purpose of this paper is to provide an overview of these developments and, more particularly, to explore their implications for the validity of empirical results obtained in earlier applications of canonical analysis. This is not intended as a criticism of these studies but rather as an attempt to further our understanding of spatial structure and process through re-examination of existing data in the light of refined techniques.