This article presents a theoretically based method for identifying the clusters of industries in which a region has a competitive advantage. The method combines cluster analysis with discriminant analysis, using variables derived from economic base theory and measures of productivity, to identify the industries in which a region has its greatest competitive advantage. These industries are called driver industries because they drive the region’s economy. The driver industries are linked to supplier and customer industries with information from a region-specific input-output model to form industry clusters. After introductory comments about cluster-based approaches to understanding regional economies, the authors present an overview of their method and the variables used. They then apply this method to the Cleveland-Akron Consolidated Metropolitan Statistical Area.

We test the null hypothesis that municipalities defined as central cities by the US Bureau of the Census in 1990 are homogeneous-a hypothesis we reject. Rather, we find that US central cities consist of 2 distinct subsets of municipalities that are aggregated from 13 cluster groupings. The article has two purposes. The first is methodological. We develop a method that uses cluster analysis to group US central cities; then we employ discriminant analysis to establish the statistical validity of those groups. We also develop techniques to minimise the role of judgement in selecting the appropriate cluster solution. The second purpose of the article is to test the substantive null hypothesis. Our rejection of the homogeneity assumption raises the spectre of specification error in research and public policies that assume homogeneity among central cities.

Examining the drivers of metropolitan economic performance, this paper models two dependent variables: change from 1990 to 2000 in gross metropolitan product and MSA employment. It is found that initial-year economic structure (an above average share of manufacturing employment), agglomeration economies, human capital (share of population with bachelor degrees or higher), and presence of state right-to-work laws are positively and significantly related to GMP and employment growth, while the economic age of the area, percentage of Black non-Hispanic residents and average wage at the beginning of the period are negatively and significantly related to both. The regional dummy variables commonly used to explain economic growth, and typically highly significant, are augmented by including climate-related amenity, business environment and economic age. When these three variables are included in the model as independent variables with the regional dummy variables, all three are significant for growth in GMP and the significance of region largely disappears.

Arresting and reversing the condition of urban distress in America's cities represents one of the most challenging and perplexing problems confronting policy-makers. Indeed, urban distress in American cities has proved to be a stubborn and largely intractable phenomenon during the past two decades. Nevertheless, a number of cities that were experiencing distress at the beginning of the 1980s are now being acclaimed as 'urban success stories' or 'revitalised' cities. We evaluate the performance, between 1980 and 1990, of these supposedly 'revitalised' cities on objective indicators of the economic well-being of their residents and compare their performance to that other cities that were equally distressed in 1980. We conclude that with the exception of Atlanta, Baltimore and Boston, the purportedly 'revitalised' cities performed no better with respect to change in the economic well-being of their residents than did other cities that were equally distressed in 1980—and in many cases performed worse.

Much has been made of the revival of distressed cities during the 1990s, yet how much of this asserted revival really worked its way down to residents? We find that residents of distressed central cities were generally worse off in 2000 than in 1980. We construct an index of the economic well-being of central city residents for the 98 central cities that had at least 125,000 residents in 1980 with metropolitan area populations of at least 250,000. We then compare the change in the economic well-being of residents of the 33 cities with the lowest index scores in 1980 against (1) their own performance over this time period, (2) the performance of the 65 non-distressed central cities, and (3) the performance of the nation. In the third section we build regression models of change in the index and of each index component to determine what explains the change in economic well-being of city residents.