First Advisor

William A. Rabiega

Date of Publication


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Urban Studies


Urban Studies and Planning




Internal Migration -- Mathematical models



Physical Description

4, xi, 228 leaves: ill. 28 cm.


In view of theoretical proliferations in migration studies, there is a need for a more comprehensive approach to migration modeling. A central problem identified in this study was the multitude of potential variables for migration research and the lack of established procedures for selecting among them. Several studies on migration have attempted to answer cornmon migration questions, but with differing variables and therefore divergent conclusions. There is thus a strong potential for misinterpretation by researchers and policy makers. Partial theories of migration have been developed rather than a unified one. This study offers an objective process through which variables may be selected for purposes of migration model design or interpreting completed studies by researchers, policy makers and others. Meta-analysis was used to develop a heuristic framework as an operational tool for selection of migration modeling options. Because meta-analysis uses past studies as its data, a wide range of previous literature was reviewed. The literature was derived from a number of disciplines, i.e., economics, sociology, geography, demography, and schools of thought within disciplines to move toward a unified modeling framework. The variables identified for meta-analytic procedure were further subjected to a factor analysis to identify the inherent variable constructs. The 1980 intrastate migration between counties in the state of Oregon was used. The data were obtained from the IRS County to County Migration Records, the County and City Data Book, and the 1980 Census of Population. Seven clusters (constructs) emerged. They included: urban amenity, low mobility, individual mobility, negative amenity, low spatial mobility, mobility, and amenity. Each cluster was representative of a partial approach. These clusters were then tested by a regression analysis by sorting them out into amenity, spatial, and mobility related variables. The two most frequently used techniques, i.e., the basic Ordinary Least Squares (OLS) and the gravity approach, were used with the same data as in factor analysis. Both OLS and the gravity approach produced a similar pattern of results. Thus, when mobility, spatial, and amenity variables were tested individually their R2 was not as high as when variables were selected from each (in spite of having the same number of variables in each). These findings have several implications. Thus a rationalized unified model, where each significant cluster is represented by a variable, allows parsimonious prediction of migration. A factor analysis is the key technique in pinpointing the minimal set of useful variables. The significance of this heuristic approach also has further implications. First, identification of an analytical structure for the development of a unified theory in migration studies. This heuristic is useful as an applied forecasting device and an academic tool in policy areas. Secondly, it provides a framework that may be useful in other social sciences’ development of theory. This modeling heuristic has some caveats. Whether an OLS or gravity model specification is used, a factor analysis of potential independent variables is an essential step. In some cases, actual data for this factor analysis may be expensive and difficult to obtain. Variables representing all clusters may not be available: irreducible specification errors are implied. Also, factor analysis requires some qualitative interpretation to elaborate clusters, both in naming them and selecting those to appear: in the reduced model. Hence, there is not a single specification from a given structure. Similarly, qualitative analysis is critical in phase I of the framework. However, in both of these instances, a wide coverage of literature provides reasonable insurance against subjective error.


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