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A method for analyzing backward recurrence time data on residential mobility.

N Baydar, M White

    Sociological Methodology
    |January 1, 1988
    PubMed
    Summary
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    This study models residential mobility using duration of residence data. Key factors influencing moves include age, homeownership, race, and children, with an unbiased method for analyzing mobility rates.

    Area of Science:

    • Demography
    • Sociology
    • Urban Studies

    Background:

    • Understanding residential mobility is crucial for urban planning and social science research.
    • Existing models often face limitations due to assumptions about the stability of underlying processes.

    Purpose of the Study:

    • To develop and apply an unbiased method for estimating duration-dependent residential mobility rates.
    • To identify key demographic and socioeconomic covariates influencing residential mobility.

    Main Methods:

    • Utilized the theory of backward recurrence times in renewal processes to infer residential mobility hazard rates.
    • Employed an unbiased finite time solution to overcome limitations of asymptotic solutions.
    • Estimated univariate and multivariate mover-stayer models using 1980 U.S. census data.
    Keywords:
    Age FactorsAmericasChildCultural BackgroundDemographic FactorsDeveloped CountriesEconomic FactorsEthnic GroupsGeographic FactorsMigrationMigration, InternalModels, TheoreticalNorth AmericaNorthern AmericaOwnershipPopulationPopulation CharacteristicsPopulation DynamicsResearch MethodologyResidence CharacteristicsSocioeconomic FactorsSpatial DistributionTime FactorsUnited StatesYouth

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    Main Results:

    • Demonstrated that asymptotic solutions can lead to severe bias if the renewal process is not stable.
    • Identified age, homeownership, race, and the presence of school-aged children as significant covariates of residential mobility.
    • Successfully estimated mover-stayer models incorporating mixture densities.

    Conclusions:

    • The developed finite time solution provides a more accurate estimation of residential mobility rates.
    • Demographic and socioeconomic factors play a critical role in explaining variations in residential mobility patterns.