Scale economies in a cooperative financial system : production credit associations



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Two Constant Elasticities of Substitution (CES) production functions are used in this study to depict the financial intermediation process of the system of Production Credit Associations (PCAs). The data utilized for estimation purposes came from Farm Credit Administration sources (published and unpublished) and a mail survey. The two production function models differ in that one provides for the inclusion of a different set of efficiency variables. Both CES models relate the two basic.inputs (capital and labor) to PCA output. The estimates of the CES homogeneity parameter derived from the analyses are used to determine whether scale economies exist within the PCA system during the data period examined. This determination is the primary purpose of the study. Other important PCA aspects addressed by this research include: the substitutability between labor and capital; the effects of PCA branching; the effects of agricultural diversification; and the effects of technology on PCA efficiency. Included in this study is a discussion of the purpose of PCAs, their relationship to the rest of the financial intermediation industry, and their operating methods. The characteristics of PCA operations, coupled with economic principles, are used to develop a viable theory of PCA objectives. It is argued that PCA objectives determine PCA behavior as to the amount and type of output produced. Two PCA output measures follow from the theory; a total measure of PCA output and a measure of PCA output per borrowing member. Each is justified on its own merit and on its interface with the developed theory. The weighted sum of major output activities serves as the basis for both output measures. Literature related to the discussed topics is referenced throughout the study. Linear estimation equations are derived from the various CES production functions. Since the coefficients of these estimation equations are algebraic expressions containing the CES parameters, their estimates are used to calculate the pertinent CES parameter estimates. The coefficients of the estimation equations are estimated by standard Ordinary Least Squares (OLS) procedures common to many multiple regression analyses.