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Notes
1. Research has been unable to establish a link between city–county consolidations and improved economic performance (Carr & Feiock, Citation1999; Feiock, Citation2004; Savitch & Vogel, Citation2004; Leland & Thurmaier, Citation2005).
2. Research has not shown evidence of city–county consolidation leading to cost savings in relation to government services (Lowery, Citation2000; Byrnes & Dollery, Citation2002; Feiock, Citation2004; Savitch & Vogel, Citation2004; Leland & Thurmaier, Citation2005).
3. I excluded consolidations that occurred before 1945 because of the nature of those contexts: first, of the eight that occurred before then, six are now, and have been for some time, completely urbanized environments—New Orleans/Orleans Parish (1805), Boston/Suffolk County (1821), Philadelphia (1854), San Francisco (1856), New York City (1874), and Denver (1902). With respect to the two remaining, one (Nantucket County, MA) is a small, suburban community of about 10,000 people and the other (Honolulu) operates under a unique policy context (i.e. Hawaii).
4. For example, Cape May County—with a population of 102, 352—was one of the initial pool of 15 possible matches for Athens-Clarke County (population 101, 489). It also had the following additional characteristics: county area, 255 square miles; urban, 85%; population change, 7.6. The comparable figures for Clarke County were 121, 93%, and 15.9, respectively. To determine Cape May's level of match, three calculations were made. First, all characteristics were converted into ratios and then totaled, as follows: (102, 352/101, 489) + (255/121) + (85/93) + (7.6/15.9) = 4.50. Next, since Clarke County's indicator would be 4.00, the first level indicator would be calculated: absolute 4.50/4.00 = 1.12. Finally, since Clarke County's first-level indicator would be 1.00, the final indicator for Cape May County would be calculated: absolute 1.00–1.12 = 0.12. This same methodology was employed for the other 14 counties that were closest to Clarke County in population. They were then ranked in decreasing order, with the county having the lowest final indicator ranked highest.
5. It is important to note that the use of population and urbanization dynamics as the matching criteria omits socio-economic and other considerations that may also have an effect on community conditions and public policy choices. In the case of two benefits, compact urban development and transportation mode diversity, the analysis includes an assessment of possible regional influences as a partial proxy for socio-economic dynamics (i.e. related to the fact that much of the development of cities in the South occurred during the height of the modern automobile era).
6. Community foundations, by virtue of their size and the composition of their boards of directors, may be perceived as being imbedded in the local power structure and not necessarily or even particularly representative of ‘bottom-up’ or grassroots citizen concerns. Moreover, citizen involvement requires more than just the participation of organized interest groups. However, according to Lowe (Citation2004), community foundations have been found to have close associations with neighborhoods. Additionally, they share two other characteristics that are the roots of their status as proxies for citizen involvement: first, they are outside the formal structure of government, thereby requiring for their involvement in the adoption of policy some sort of participatory process; and second, they are driven by quality-of-life concerns that would most probably find broad support among a community's citizenry.
7. Nine of the 11 consolidated counties are in the South, as this region is defined by the US Census Bureau. As suggested in Ewing et al. (Citation2002), there is evidence that urbanization in this region tends to be more sprawled than in other regions. To account for this possible regional effect, southern consolidated counties were compared with other southern counties. Regression results strongly indicate a positive association between population density and size (p<0.001 for the independent variable, population density of urbanized area, when regressed against population of urbanized area, n = 452). To account for this possibility, counties were grouped and compared according to population size.