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Natural amenities and their effects on migration along the urban–rural continuum

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Abstract

The importance of natural amenities in promoting development and population change has been addressed in a large body of literature with recent studies identifying rather obvious spatial heterogeneity in the effects of natural amenities on migration. This said, the potential variation along the urban–rural continuum has not been addressed empirically. In this study, we examine and compare the migration effects of natural amenities in five specific urban–rural continuum types at the minor civil division level in the US state of Wisconsin. Results of spatial analysis suggest that natural amenities do indeed have differing effects on migration along this urban–rural continuum. Overall, natural amenities have the largest effect on in-migration into rural areas adjacent to metro areas and no effect on in-migration into urban areas. The effects of natural amenities on in-migration into remote rural areas rely more on growth trends within these regions. These findings have important implications for rural development, land use policy, and natural resource management.

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Notes

  1. Because this study is focused on MCD level empirical analysis for only one state, findings are limited and may be appropriately generalizable to regions that share similar economic, demographic, and social contexts (e.g., the Lake States of Minnesota, Wisconsin, and Michigan). Also, MCD analysis in a single state does not allow for a comprehensive spectrum of variation among alternative natural amenity types. Applying this finely grained geographic scale approach to multistate regions will likely generate more robust findings and remains for future research.

  2. The findings may not apply to other time periods with different population redistribution patterns. Studying the spatial variation of migration effects of natural amenities over several time periods in future research will provide a more comprehensive understanding of the phenomenon.

  3. A visual examination of the 14 MCDs indicated that their bordering MCDs were well qualified as urban areas.

  4. Natural amenities could be highly correlated as some variables, such as forests and wetlands, overlay one another. In this study, the Pearson’s correlations between the seven natural amenity variables (in absolute values) are all below 0.4. When examining the correlations separately in the five types of areas, all

    correlations (in absolute values) are below 0.5, except in urban areas, where the correlation between forests and viewsheds and the one between forests and wetlands are 0.55 and 0.57. These correlations may reduce the robustness of coefficient estimates, but likely to a lesser extent, as most correlations are modest.

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Author information

Authors and Affiliations

  1. Department of Sociology and Social Science Research Center, Mississippi State University, PO Box C, Starkville, MS, 39762, USA

    Guangqing Chi

  2. Department of Urban and Regional Planning, University of Wisconsin-Madison, Madison, WI, 53706, USA

    David W. Marcouiller

Authors
  1. Guangqing Chi
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Correspondence to Guangqing Chi.

Appendix: The control indices

Appendix: The control indices

These indices are obtained from the Chi and Marcouiller (2009a) study and the Chi (2010a) study. The indices of demographics, livability, and accessibility are measured as of 1990 using principal factor analysis by varimax rotation with Kaiser criterion. Their variables as well as corresponding factor loadings are presented in the tables below. The land developability index measures the proportion of lands available for development. The index is generated by the spatial overlay method based on water, wetlands, slope (>20 %), public lands, and built-up lands, which are seen as undevelopable; the spatial overlay method provides more accurate estimates of land developability than existing aggregation and weights approaches do. Please see Chi and Marcouiller (2009a) and Chi (2010a) for detail on the indices.

Demographic variables

Factor loadings

Factor 1 (age structure)

Factor 2 (race)

Variance explained

31.43 %

28.72 %

Population density

-0.264

0.437

Young

0.784

-0.008

Old

-0.559

-0.013

Blacks

0.084

0.592

Hispanics

0.031

0.470

 

Livability variables

Factor loadings

Factor 1 (wealth and education)

Factor 2 (modernization)

Factor 3 (luxury)

Variance explained

26.31 %

15.20 %

11.79 %

Unemployment rate

0.379

\(-\)0.065

0.447

School performance

0.210

0.035

\(-\)0.023

Crime rate

0.390

0.120

\(-\)0.141

Income

0.882

\(-\)0.208

0.223

High school education

0.728

\(-\)0.020

0.090

Bachelor’s degree

0.771

0.116

\(-\)0.038

College students

0.394

0.201

0.063

Female-headed households

\(-\)0.084

0.352

\(-\)0.006

Public water

0.094

0.832

0.152

New housing

\(-\)0.370

\(-\)0.028

0.604

Seasonal housing

\(-\)0.217

\(-\)0.214

\(-\)0.661

Real estate value

0.875

\(-\)0.130

\(-\)0.048

County seat status

0.008

0.371

0.050

Retail

0.139

0.413

\(-\)0.321

Agriculture

\(-\)0.367

\(-\)0.650

0.460

 

Accessibility variables

Factor loadings

Factor 1 (proximity and infrastructure)

Factor 2 (public transportation)

Variance explained

29.72 %

15.02 %

Residential preference

0.460

0.141

Accessibility to airports

0.348

0.171

Accessibility to highways

\(-\)0.008

0.049

Highway infrastructure

0.781

0.119

Journey to work

0.134

0.472

Public transportation

0.297

\(-\)0.071

Buses

0.631

\(-\)0.099

  1. Source: Chi and Marcouiller (2009a), Table 2

 

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Chi, G., Marcouiller, D.W. Natural amenities and their effects on migration along the urban–rural continuum. Ann Reg Sci 50, 861–883 (2013). https://doi.org/10.1007/s00168-012-0524-2

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