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The body of research purporting to explain or identify the determinants of local economic development policy choice using analysis of survey data is also problematic. It tends to offer con- tradictory findings based on variation in the questionnaires used and hence data collected, operationalization of indicators, mix of variables examined, statistical technique employed, and the sample drawn. Furthermore, extant models have explained widely varying amounts of policy behavior. For example, Green and Fleischmann (1991) accounted for .159 to .285 of the variance in economic development activities, whereas Reese (1991, 1992, 1997) accounted for .05 to .89 of the variance in policy choices, depending on the particular type of policy examined. The extent of vari- ation supports the notion that different research methodologies and samples have gotten in the way of explanation. As Wolman (1996) noted, “With so many different model specifications, it is extremely difficult to assess the impact of any of the variables across studies” (p. 126). The prepon- derantly low explanatory power of current models suggests another problem: There are clearly important variables or dynamics missing from the analysis.

Many interesting and indeed critical dynamics may be missed in such cross-sectional analyses. Perhaps, the mixed results and limited explanatory power of past research lie within the cities themselves. Historical trends or even more idiosyncratic causes of policies may be hiding under our noses, so to speak, buried in macro and static analytic methodologies. A potentially more fruit- ful avenue for understanding such local decisions may lie in analysis of the history, characteristics, personnel, and operating forces in cities that use particular policies. The need is to describe the unique character of each community that produces such a decision, stepping back from the macro analysis and seeking patterns among the idiosyncrasies. If, as it appears, current theories provide only partial explanations, then perhaps deconstructing the analysis to the case level will permit reconstructing more robust theories.


It seems clear that a combined methodology that takes advantage of the strengths of both sur- veys and case studies would be the optimal approach to many research questions. However, this has not been done in the economic development literature to date. Although obviously more expensive and labor intensive, the combination of methodologies offers a unique and potentially powerful research approach. This research rests on findings from both a large survey database and compara- tive case studies. The surveys were sent in the spring of 1994 to the chief executive officer (CEO) of all cities in Canada with a population of more than 10,000 and of all U.S. border-state cities (with Canada) meeting the same population criteria. The CEOs were asked to forward the survey to the individual responsible for economic development or answer it themselves, if appropriate.2

. . . a combined methodology that takes advantage of the strengths of both surveys and case studies would be the optimal approach to many research questions.

From that data set, nine cities were selected for intensive case studies. Selection was purposive, based on an extensive knowledge base about the cities and on statistical cluster analysis. The case cities were drawn from two states (Michigan and Ohio) and one province (Ontario). The most obvi- ous reason for this choice was ease of analysis because the locations were all relatively proximate to the researchers. More important, it was determined better to have more cities within the same state or province than to increase the number of state/provincial jurisdictions. Previous research has pointed to the importance of state and provincial enabling legislation on local economic devel- opment policy choice (Reese, 1997; Reese & Malmer, 1994). In short, the enabling environment at the state or provincial level determines permissible local development policies. Selecting cities from the same state or province controls for enabling legislation as well as for other economic, structural, and cultural forces that may vary along state or provincial lines. Having multiple cities within the same state/province allows for some certainty that any internal variation results from dif- ferences in the cities themselves rather than in the larger environment.

Cluster analysis was then used to identify different types of cities based on a number of variables found to be related to economic development policy. These included the extent of community and business input into decision making, intercity competition for economic development, extent of local planning and evaluation, residential need for services, change in unemployment, and govern- mental structure.3 The case cities are described more fully in Appendix A; they include Allen Park,

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