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Erschienen in: The Annals of Regional Science 2/2006

01.06.2006 | Original Paper

Growth and change in U.S. micropolitan areas

verfasst von: Gordon F. Mulligan, Alexander C. Vias

Erschienen in: The Annals of Regional Science | Ausgabe 2/2006

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Abstract

The U.S. Census Bureau has now recognized micropolitan places, which are sometimes called emerging metropolitan areas or mini-metros. After the 1990 census, a total of 581 different non-metropolitan counties, forming 496 consolidated micropolitan areas, were assigned to this new settlement category. The first half of the paper analyzes the evolving geographic distribution and the shifting employment attributes (emphasizing job specialization) of these places during 1980–2000. Changes in the U.S. micropolitan landscape, reflecting the impressive growth of these places during the late 20th century, mirror other well-known national demographic and economic trends. The second half of the paper analyzes simultaneous population and employment change in micropolitan counties, using a series of partial adjustment models that control for various demographic, economic, and geographic factors. Evidently (initial and adjusted) population levels have induced both employment and population change in these places, but employment levels have failed to have the same impact.

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Fußnoten
1
Thomas (1990) originally identified a total of 219 micropolitan places using 1986 population data. Somewhat later Heubusch (1997) identified only 193 of these places using 1994 population data, claiming that 52 of the original 219 places had already ascended to metropolitan status by 1990.
 
2
We have used a list of official micropolitan counties released earlier by the Census Bureau. The latest release actually designates 679 counties and 565 areas.
 
3
One strange attribute of the preliminary list of micropolitan counties identified by the Census Bureau is that some counties were actually metropolitan. See Table 2 to assess the frequencies. Heubsuch actually anticipated this issue when he updated the list of micropolitan counties first proposed by Thomas.
 
4
It is often difficult to gauge the best data to analyze when the observation units—in this case, micropolitan counties—are themselves changing in fundamental ways over a lengthy period of time. For our purposes we certainly wanted to use a panel data set that had the same number of observation units through time. But, at the same time, we wanted a certain amount of uniformity (or lack of volatility) in how these units evolved. The problem is not a new one, and has been extensively discussed in the population literature (e.g., see Fitzimmons et al. 1980; Heaton et al. 1981; Johnson 1989).
 
5
Classification, whether done for general or for special purposes, is very important in every branch of science. But in those social sciences closely aligned with the humanities, classification has been looked upon with some suspicion—and, in some quarters, even derision—during recent times. Fortunately this view is not very popular in regional science. In any case, the designation of separate types, by whatever procedures, will always involve subjectivity and will always invite contestability (e.g., see Johnston 1968; Eldredge 1998).
 
6
Multivariate procedures tend to minimize classification problems related to the use of somewhat arbitrary cut-off points. This issue, which especially interested one referee, was addressed by Smith (1965), among others, quite some time ago. Accordingly we modified our original classification system to allow for multiple specializations. It is important to clarify that we wanted a typology where the uniformity of groups was based more on industry-specific employment surpluses than on overall employment profiles. One very useful aspect of our univariate classification is its longitudinal stability, which was very evident in tables we placed in the previous version of the paper. At least since King (1966), it has been known that multivariate classifications involving geographic units can exhibit very different group memberships over time. This was certainly true in the present instance where, using a wide variety of different procedures, we always generated very different multivariate groupings of the U.S. micropolitan counties in 1980, 1990, and 2000.
 
7
Non-earnings income could not be accurately allocated for some other Virginia counties, so the number of observations declined from 577 to 566 in the regressions of Sections 5 and 6.
 
8
Natural logarithms are used for this specification. The dependent variable is the difference in the logarithms (of employment or population) between time t and time t−1. All non-dummy independent variables are transformed. This specification allows coefficients to be interpreted as elasticities and reduces heteroskedasticity in the data.
 
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Metadaten
Titel
Growth and change in U.S. micropolitan areas
verfasst von
Gordon F. Mulligan
Alexander C. Vias
Publikationsdatum
01.06.2006
Verlag
Springer-Verlag
Erschienen in
The Annals of Regional Science / Ausgabe 2/2006
Print ISSN: 0570-1864
Elektronische ISSN: 1432-0592
DOI
https://doi.org/10.1007/s00168-005-0034-6

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