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Data on production and management practices across firms are scarce.Therefore studies of rural and urban industrial development have usually abstracted from this issue or have invoked product cycle theory to explain comparative urban and rural productive ingenuity. Several qualitative studies of rural production have contradicted the product cycle hypotheses, but attempts to generalize these results have not succeeded. An attempt is made here to test the product cycle explanation of industrial location using the registry of firms with ISO 9000 certification in nine southern states. These data suggest a bifurcation of rural counties. Urban and rural counties containing certified firms have been much more likely to increase the number of such firms, but in those counties without certified firms the probability of new certified firms has declined

ABSTRACT The basic premise of this article is that the historic location determinants literature is unduly pessimistic regarding the economic prospects of rural areas. Most historic location research has treated rural areas as homogeneous regions. This study demonstrates that rural counties should be treated as differentiated sets of economic environments rather than as an aggregate. The locational potential of specific industries differs dramatically among differentiated rural regions. When examined in this way, a number of high‐growth industries surface as having development potential under specified rural conditions. In addition this work raises serious questions about the adequacy of product life‐cycle theory (Erickson 1976) and high‐technology filtering‐down theory (Glasmeier 1991) in identifying the variables critical to industrial location. This work indicates that neither small size nor remoteness is as limiting as suggested by earlier research.

AbstractThis research adapts the Neyman‐Pearson testing protocol commonly used in biomedical research for ex post evaluation of the employment impacts of new ethanol bio‐refineries in the U.S. Great Plains and the Midwest. By calculating the power of the test, the suggested protocol may provide policy‐relevant information, even in the event of nonsignificant findings. The main obstacle to applying this protocol has been the need to posit an explicit alternative distribution, which runs counter to the empiricist tradition of mainstream econometrics. We resolve this problem by applying a data generating process with known parameters anchored to sample data to compute power.